Note: Descriptions are shown in the official language in which they were submitted.
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PROACTIVE USER INTERFACE INCLUDING EVOLVING AGENT
BACKGROUND OF INVENTION
1. Field of the Invention
The present invention relates to a proactive user interface including an
evolving agent, and systems and methods thereof, particularly for use with
mobile
information devices.
1. Description of Related Art
The use of mobile and portable wireless devices has expanded
dramatically in recent years. Many such devices having varying functions,
internal resources, and capabilities now exist, and include, but are not
limited to,
mobile telephones, personal digital assistants, medical and laboratory
instrumentation, smart cards, and set-top boxes. All such devices can be
refered to
are mobile information devices. The devices tend to be special purpose,
limited-
function devices, rather than the general-purpose personal computer. Many of
these devices are connected to the Internet, and are used for a variety of
applications.
One example of such a mobile information device is the cellular telephone.
Cellular telephones are fast becoming ubiquitous; and the use of cellular
telephones is even surpassing that of traditional PSTN (public switched
telephone
network) telephones or "land line" telephones. Cellular telephones themselves
are becoming more sophisticated, and in fact are actually computational
devices
with embedded operating systems.
As cellular telephones become more sophisticated, the range of functions
that they offer is also potentially becoming more extensive. However,
currently
available functions are typically related to extensions of functions already
present
in regular (land line) telephones, and/or the merging of certain functions of
personal digital assistants (PDAs) with those of cellular telephones. The user
interface provided with cellular telephones is similarly non-sophisticated,
typically featuring a keypad for scrolling through a few simple menus.
Customization, although clearly desired by customers who have spent
significant
amounts of money on personalized ring tones and other cellular telephone
accessories, is still limited to a very few functions of the cellular
telephone.
Furthermore, cellular telephones currently lack any automatic personalization,
for
example the user interface and custom/tailored functionalities that are
required for
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better use of the mobile information device, and/or the ability to react
according
to the behavior of the user.
This lack of sophistication, however, is also seen with user interfaces for
personal (desk top or laptop) computers and other computational devices. These
computational devices can also only be customized in very simple ways. Such
customization must be performed by the user, who may not understand computer
functions and/or may not feel comfortable with performing such customization
tasks. Currently, computational devices do not learn patterns of user behavior
and adjust their own behavior accordingly, as adaptive systems for the user
interface. If the user cannot manually adjust the computer, then the user must
adjust his/her behavior to accommodate the computer, rather than vice versa.
Software which is capable of learning has been developed, albeit only for
specialized laboratory functions. For example, "artificial intelligence" (AI)
software has been developed. The term "AI" has been given a number of
definitions. "Al is the study of the computations that make it possible to
perceive,
reason, and act."(Artificial Intelligence A Modern Approach (second edition)
by
Stuart Russell , Peter Norvig (Prentice Hall, Pearson Education Inc, 2003). Al
software combines several different concepts, such as perception, which
provides
an interface to the world in which the Al software is required to reason and
act.
Examples include but are not limited to, natural language processing ¨
communicating, understanding document content and context of natural language;
computer vision ¨ perceive objects from imagery source; and sensor systems ¨
perception of objects and features of perceived objects analyzing sensory
data, etc.
Another important concept is that of the knowledge base. Knowledge
representation is responsible for representing, extracting and storing
knowledge.
This discipline also provides techniques to generalize knowledge, feature
extraction and enumeration, object state construction and definitions. The
implementation itself may be performed by commonly using known data
structures, such as graphs, vectors, tables, etc.
Yet another important concept is that of reasoning. Automated reasoning
combines the algorithms that use the knowledge representation and perception
to
draw new conclusions, infer questions and answers, and achieve the agent
goals.
The following conceptual frameworks are examples of AT reasoning: rule bases ¨
system rules are evaluated against the knowledge base and perceived state for
reasoning; search systems ¨ the use of well known data structures for
searching
for an intelligent conclusion according to the perceived state, the available
knowledge and goal (examples include decision trees, state graphs, minimax
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decision etc); classifiers ¨ the target of the classifier reasoning system is
to
classify a perceived state represented as an experiment that has no
classification
tag. According to a pre-classified knowledge base the classifier will infer
the
classification of the new experiment (examples include vector distance
heuristics,
Support Vector Machine, Classifier Neural Network etc).
Another important concept is for learning. The target of learning is
improving the potential performance of the AT reasoning system by
generalization
over experiences. The input of a learning algorithm will be the experiment and
the
output would be modifications of the knowledge base according to the results
(examples include Reinforcement learning, Batch learning, Support Vector
Machine etc).
Work has also been done for genetic algorithms and evolution algorithms
for software. One example of such software is described in "Evolving
Virtual
Creatures", by Karl Sims (Computer Graphics, SIGGRAPH '94 Proceedings, July
1994, pp. 15-22). This reference described software "creatures" which could
move through a three-dimensional virtual world, which is a simulated version
of
the actual physical world. The creatures could learn and evolve by using
genetic
algorithms, thereby changing their behaviors without directed external input.
These genetic algorithms therefore delineated a hyperspace of potential
behaviors
having different "fitness" or rewards in the virtual world. The algorithms
themselves were implemented by using directed graphs, which describe both the
genotypes (components) of the creatures, and their behavior.
At the start of the simulation, many different creatures with different
genotypes are simulated. The creatures are allowed to alter their behavior in
response to different stimuli in the virtual world. At each "generation", only
certain creatures are allowed to survive, either according to a relative or
absolute
cut-off score, with the score being determined according to the fitness of the
behavior of the creatures. Mutations are permitted to occur, which may
increase
the fitness (and hence survivability) of the mutated creatures, or vice versa.
Mutations are also performed through the directed graph, for example by
randomly changing a value associated with a node, and/or adding or deleting
nodes. Similarly, "mating" between creatures may result in changes to the
directed graph.
The results described in the reference showed that in fact virtual creatures
could change and evolve. However, the creatures could only operate within
their virtual world, and had no point of reference or contact with the actual
physical world, and/or with human computer operators.
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SUMMARY OF THE INVENTION
The background art does not teach or suggest a system or method for
enabling intelligent software at least for mobile information devices to learn
and
evolve specifically for interacting with human users. The background art also
does not teach or suggest an intelligent agent for a mobile information
device,
which is capable of interacting with a human user through an avatar. The
background art also does not teach or suggest a proactive user interface for a
mobile device, in which the proactive user interface learns the behavior of
the
user and is then able to actively suggest options for evolution of the agent
to the
user. The background art also does not teach or suggest an agent for a mobile
information device, which uses an avatar to interact with another avatar of
another mobile information device or the user thereof.
The background art does not teach or suggest a system or method for
enabling intelligent software at least for mobile information devices to
express an
emotion specifically for interacting with human users. The background art also
does not teach or suggest a proactive user interface for a computational
device, in
which the proactive user interface learns the behavior of the user and is then
able
to actively suggest options to the user and express an emotion according to a
reaction of the user to the suggestion. The background art also does not teach
or
suggest an intelligent agent for a mobile information device, which can
perform
interaction with a human user through an avatar, said interaction including
emotional expression.
The present invention overcomes these deficiencies of the background art
by providing a proactive user interface, which could optionally be installed
in (or
otherwise control and/or be associated with) any type of computational device.
The proactive user interface would actively suggest options for evolution of
the
agent to the user, based upon prior experience with a particular user and/or
various preprogrammed patterns from which the computational device could
select, depending upon user behavior. In addition, the proactive user
interface
would actively make suggestions to the user, and/or otherwise engage in non-
deterministic or unexpected behavior, based upon prior experience
(interaction)
with a particular user and/or various preprogrammed patterns from which the
computational device could select, depending upon user behavior. These
suggestions could optionally be made by altering the appearance of at least a
portion of the display, for example by changing a menu or a portion thereof;
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providing different menus for display; and/or altering touch screen
functionality.
The suggestions could also optionally be made audibly. Other types of
suggestions or delivery mechanisms are possible. The present invention
features
the expression of an emotion of the agent according to a reaction of the user
to
such a suggestion.
By "suggestion" it should be noted that the system could actually execute
the action automatically and express a corresponding emotion of the agent,
given
certain user preferences and also depending upon whether the system state
allows
the specific execution of the action. The present invention is characterized
in that
the emotional expression of the agent depends upon whether or not the user
makes a selection in response to a "suggestion" of the agent or upon the
user's
rewards to the suggestion of the agent.
Generally, it is important to emphasize that the proactive user interface
preferably at least appears to be intelligent and interactive, and is
preferably
capable of at least somewhat "free" (e.g. non-scripted or partially scripted)
communication with the user. An intelligent appearance is important in the
sense
that the expectations of the user are preferably fulfilled for interactions
with an
"intelligent" agent/device. These expectations may optionally be shaped by
such factors as the ability to communicate, the optional appearance of the
interface, the use of anthropomorphic attribute(s) and so forth, which are
preferably used to increase the sense of intelligence in the interactions
between
the user and the proactive user interface. In terms of communication received
from the user, the proactive user interface is preferably able to sense how
the user
wants to interact with the mobile information device.
Optionally,
communication may be in only one direction; for example, the interface may
optionally present messages or information to the user, but not receive
information from the user, or alternatively the opposite may be implemented.
Preferably, communication is bi-directional for preferred interactions with
the
user.
For communication to the user, the proactive interface is capable of
displaying or demonstrating simulated emotions for interactions with the user,
as
part of communication with the user. As described in greater detail below,
these
emotions are simulated for presentation by an intelligent agent, represented
by an
avatar or creature. The emotions are created through an emotional system,
which may optionally be at least partially controlled according to at least
one user
preference. The emotional system is used in order for the reactions and
communications of the intelligent agent to be believable in terms of the
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perception of the user; for example, if the intelligent agent is presented as
a dog-
like creature, the emotional system enables the emotions to be consistent with
the
expectations of the user with regard to "dog-like" behavior.
Similarly, the intelligent agent at least appears to be intelligent to the
user.
The intelligence may optionally be provided through a completely deterministic
mechanism; however, the basis for at least the appearance of intelligence
includes
at least one or more random or semi-random elements. Again, such elements are
present in order to be consistent with the expectations of the user concerning
intelligence with regard to the representation of the intelligent agent.
Adaptiveness is preferably present, in order for the intelligent agent to be
,
able to alter behavior at least somewhat for satisfying the request or other
communication of the user. Even if the proactive user interface does not
include
an intelligent agent for communicating with the user, adaptiveness enables the
interface to be proactive. Observation of the interaction of the user with the
mobile information device enables such adaptiveness to be performed, although
the reaction of the proactive user interface to such observation may be guided
by
a knowledge base and/or a rule base.
As a specific, non-limiting but preferred example of such adaptiveness,
particularly for a mobile information device which includes a plurality of
menus,
such adaptiveness may include the ability to alter at least one aspect of the
menu.
For example, one or more shortcuts may be provided, enabling the user to
directly
reach a menu choice while by-passing at least one (and more preferably all) of
the
previous menus or sub-menus which are higher in the menu hierarchy than the
final choice. Optionally (alternatively or additionally), one or more menus
may
be rearranged according to adaptiveness of the proactive user interface, for
example according to frequency of use. Such a rearrangement may include
moving a part of a menu, such as a menu choice and/or a sub-menu, to a new
location that is higher in the menu hierarchy than the current location. Sub-
menus which are higher in a menu hierarchy are reached more quickly, through
the selection of fewer menu choices, than those which are located in a lower
(further down) location in the hierarchy.
Adaptiveness and/or emotions are assisted through the use of rewards for
learning by the proactive user interface. Suggestions or actions of which the
user approves provide a reward, or a positive incentive, to the proactive
interface
to continue with such suggestions or actions; disapproval by the user causes a
disincentive to the proactive user interface to continue such behavior(s).
Providing positive or negative incentives/disincentives to the proactive user
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interface preferably enables the behavior of the interface to be more nuanced,
rather than a more "black or white" approach, in which a behavior would either
be permitted or forbidden. Such nuances are also preferred to enable opposing
or contradictory behaviors to be handled, when such behaviors are collectively
approved/disapproved by the user to at least some extent.
According to an embodiment of the present invention, a model of the user
is constructed through the interaction of the proactive user interface with
the user.
Such a model would integrate Al knowledge bases determined from the behavior
of the user and/or preprogrammed. Furthermore, the model would also enable
the proactive user interface to gauge the reaction of the user to particular
suggestions made by the user interface, thereby adapting to the implicit
preferences of the user.
Non-limiting examples of such computational devices include automated
teller machines (ATM's) (this also has security implications, as certain
patterns of
user behavior could set off an alarm, for example), regular computers of any
type
(such as desktop, laptop, thin clients, wearable computers and so forth),
mobile
information devices such as cellular telephones, pager devices, other wireless
communication devices, regular telephones having an operating system, PDA's
and wireless PDA's, and consumer appliances having an operating system.
Hereinafter, the term "computational device" includes any electronic device
having an operating system and being capable of performing computations. The
operating system may be an embedded system and/or another type of software
and/or hardware run time environment.
Hereinafter, the term "mobile
information device" includes, but is not limited to, any type of wireless
communication device, including, but not limited to, cellular telephones,
wireless
pagers, wireless PDA's and the like.
The present invention is implemented in order to provide an enhanced user
experience and interaction with the computational device, as well as to change
the
current generic, non-flexible user interface of such devices into a flexible,
truly
user friendly interface. More preferably, the present invention is implemented
to
provide an enhanced emotional experience of the user with the computational
device, for example according to the optional but preferred embodiment of
constructing the user interface in the form of an avatar which would interact
with
the user. The present invention is therefore capable of providing a "living
device" experience, particularly for mobile information devices such as
cellular
telephones, for example. According to this embodiment, the user may even
form an emotional attachment to the "living device".
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According to another embodiment of the present invention, there is
provided a mobile information device which includes an adaptive system. Like
the user interface above, it also relies upon prior experience with a user
and/or
preprogrammed patterns. However, the adaptive system is more restricted to
operating within the functions and environment of a mobile information device.
Either or both of the mobile information device adaptive system and
proactive user interfaces may be implemented with genetic algorithms,
artificial
intelligence (AI) algorithms, machine learning (ML) algorithms, learned
behavior,
and software/computational devices which are capable of evolution. Either or
both may also provide an advanced level of voice commands, touch screen
commands, and keyboard 'short-cuts'.
According to another preferred embodiment of the present invention,
there is provided one or more intelligent agents for use with a mobile
information
device over a mobile information device network, preferably including an
avatar
(or "creature"; hereinafter these terms are used interchangeably) through
which
the agent may communicate with the human user. The avatar can provide a user
interface for interacting with the user. The intelligent agent can also
include an
agent for controlling at least one interaction of the mobile information
device
over the network. This embodiment may include a plurality of such intelligent
agents being connected over the mobile information device network, thereby
forming a network of such agents. Various applications may also be provided
through this embodiment, including but not limited to teaching in general
and/or
for learning how to use the mobile information device in particular, teaching
languages, communication applications, community applications, games,
entertainment, shopping (getting coupons, etc), locating a shop or another
place,
filtering advertisements and other non-solicited messages, role-playing or
other
interactive games over the cell phone network, "chat" and meeting functions,
the
ability to buy "presents" for the intelligent agents and otherwise accessorize
the
character, and so forth. In theory, the agents themselves could be given
"pets" as
accessories.
The intelligent agents could also assist in providing various
business/promotional opportunities for the cell phone operators. The agents
could also assist with installing and operating software on cell phones, which
is a
new area of commerce. For example, the agents could assist with the
determination of the proper type of mobile information device and other
details
that are essential for correctly downloading and operating software.
Therefore, a number of different interactions are possible according to the
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various embodiments of the present invention. These interactions include any
one or
more of an interaction between the user of the device and an avatar or other
character or
personification of the device; an interaction between the user of the device
and the
device, for operating the device, through the avatar or other character or
personification;
interactions between two users through their respective devices, by
communicating
through the avatar, or other character or personification of the device; and
interactions
between two devices through their respective intelligent agents, and can be
done without
any communication between users or even between the agent and the user. The
interaction or interactions that are possible are determined according to the
embodiment
of the present invention, as described in greater detail below.
The present invention benefits from the relatively restricted environment of a
computational device and/or a mobile information device, such as a cellular
telephone for
example, because the parameters of such an environment are known in advance.
Even if
such devices are communicating through a network, such as a cellular telephone
network
for example, the parameters of the environment can still be predetermined. The
current
computational devices only provide a generic interface, with little or no
customization
permitted by even manual, direct intervention by the user.
It should be noted that the term "software" may also optionally include
firmware
or instructions operated by hardware.
According to an aspect of the present invention, there is provided a proactive
user
interface in combination with a computational device having an operating
system, the
proactive user interface comprising:
(a) an interface unit for communication between a user of the proactive user
interface and said operating system, said interface unit including an evolving
avatar
enabling communication with the user; and
(b) a learning module for detecting at least one pattern of interaction of the
user
with said interface unit, and actively suggesting, to the user, options for
evolving at least
one function of the proactive user interface according to said at least one
pattern by at
least one of altering an appearance of at least a portion of a display,
changing a menu or a
potion thereof, and altering touch screen functionality;
wherein, if a suggestion or an action provided by the learning module is
approved
by the user, the interface unit provides positive incentive to the proactive
user interface, if
the suggestion or the action provided by the learning module is disapproved by
the user,
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the interface unit provides a disincentive to the proactive user interface,
and wherein at
least one of the positive incentive and the disincentive are used in
determining a state of
the evolving avatar.
According to another aspect of the present invention, there is provided a
proactive
user interface in combination with a computational device having an operating
system,
the proactive user interface comprising:
(a) an interface unit for providing a user interface and communicating between
a
user of the proactive user interface and said operating system, said interface
unit
including an emotional agent for communicating with the user;
(b) at least one software application controlled by the operating system;
(c) an artificial intelligence (Al) framework unit for supporting said at
least one
software application, communicating with a host platform having the operating
system,
detecting at least one pattern of interaction of the user with said interface
unit, actively
suggesting, to the user, options for altering at least one function of the
proactive user
interface according to said detected pattern and altering at least one
function of the
proactive user interface according to an option selected by the user among the
suggested
options,
wherein said agent expresses at least one emotion according to a reaction of
the
user to said suggestion,
wherein, if a suggestion or an action provided by the Al framework unit is
approved by the user, the interface unit provides positive incentive to the
proactive user
interface, if the suggestion or the action provided by the Al framework unit
is
disapproved by the user, the interface unit provides a disincentive to the
proactive user
interface, and wherein at least one of the positive incentive and the
disincentive are used
in determining a state of the evolving avatar.
BRIEF DESCRIPTION OF THE DRAWINGS
The invention is herein described, by way of example only, with reference to
the
accompanying drawings, wherein:
FIG. 1 is a block diagram of an exemplary learning module according to the
present invention;
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FIG. 2 is a block diagram of an exemplary system according to the present
invention for using the proactive user interface;
FIG. 3 shows an exemplary implementation of a proactive user interface system
according to the present invention;
FIG. 4 is a block diagram of an exemplary implementation of the adaptive
system
according to the present invention;
FIGS. 5A and 5B are a block diagram and a sequence diagram, respectively, of
an
exemplary application management system according to the present invention;
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FIGS. 6A and 6B show an exemplary infrastructure required for the
adaptive system according to the present invention to perform one or more
actions through the operating system of the mobile information device and an
exemplary sequence diagram thereof according to the present invention;
FIGS. 7A, 7B and 7C show exemplary events, and how they are handled
by interactions between the mobile information device (through the operating
system of the device) and the system of the present invention;
FIGS. 8A and 8B describe an exemplary structure of the intelligent agent
and also includes an exemplary sequence diagram for the operation of the
intelligent agent;
FIGS. 9A and 9B show two exemplary methods for selecting an action
according to the present invention;
FIG 10 shows a sequence diagram of an exemplary action execution
method according to the present invention;
FIGS. 11A, 11B and 11C are diagrams for describing an exemplary,
illustrative implementation of an emotional system according to the present
invention;
FIG. 12A shows an exemplary sequence diagram for textual
communication according to the present invention;
FIG. 12B shows a non-limitting example of an emotional expression "I am
happy" that the agent performs in a mobile phone;
FIGS. 13A, 13B and 13C show an exemplary evolution class diagram, and
an exemplary mutation and an exemplary hybrid sequence diagram, respectively,
according to the present invention;FIG. 14 shows an exemplary hybridization
sequence between intelligent agents on two mobile information devices;
FIGS. 15, 16, 17, 18, 19, 20 and 21 show exemplary screenshots of an
avatar or creature according to different embodiments of the present
invention;
FIG. 22 is a block diagram of an exemplary intelligent agent system
according to the present invention;
FIG. 23 shows the system of Figure 23 in more detail;
FIG. 24 is a block diagram of an exemplary implementation of an action
selection system according to the present invention; and
FIGS. 25A and 25B show exemplary screenshots of the avatar according to
the present invention on the screen of the mobile information device.
DESCRIPTION OF THE PREFERRED EMBODIMENTS
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Preferred embodiments of the present invention will be described herein
below with reference to the accompanying drawings. In the following
description,
well-known functions or constructions are not described in detail since they
would obscure the invention in unnecessary detail.
The present invention is of a proactive user interface, which could be
installed in (or otherwise control and/or be associated with) any type of
computational device. The proactive user interface actively makes suggestions
to the user, based upon prior experience with a particular user and/or various
preprogrammed patterns from which the computational device could select,
depending upon user behavior. These suggestions could optionally be made by
altering the appearance of at least a portion of the display, for example by
changing a menu or a portion thereof; providing different menus for display;
and/or altering touch screen functionality. The suggestions could also be made
audibly. The present invention features the expression of an emotion of the
agent according to a reaction of the user to such a suggestion.
The proactive user interface is preferably implemented for a
computational device, as previously described, which includes an operating
system. The interface can include a user interface for communicating between
the user and the operating system. The interface can also include a learning
module for detecting at least one pattern of interaction of the user with the
user
interface and for actively suggesting options for evolution of at least one
function
of the user interface to the user, according to the detected pattern.
Therefore, the
proactive user interface can anticipate the requests of the user and thereby
assist
the user in selecting a desired function of the computational device.
At least one pattern can be selected from the group consisting of a pattern
determined according to at least one previous interaction of the user with the
user
interface, and a predetermined pattern, or a combination thereof. The first
type
of pattern represents learned behavior, while the second type of pattern may
be
preprogrammed or otherwise predetermined, particularly for assisting the user
when a particular computational device is first being operated by the user. A
third type of pattern could combine these two aspects, and would enable the
pattern to be at least partially determined according to the user behavior,
but not
completely; for example, the pattern selection may be guided according to a
plurality of rules, and/or according to a restrictive definition of the
possible world
environment state and/or the state of the device and/or user interface. The
pattern includes a pattern of the user's preferences for the appearance,
function or
characteristic of the intelligent agent.
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The user interface preferably features a graphical display, such that at least
one function of the graphical display is proactively altered according to the
pattern. For example, at least a portion of the graphical display may be
altered,
for example by selecting a menu for display according to the detected pattern;
and
displaying the menu. The menu may be selected by constructing a menu from a
plurality of menu options, for example in order to create a menu "on the fly".
The user interface may feature an audio display, such that altering at least
one function of the user interface involves altering at least one audible
sound
produced by the computational device.
The proactive user interface could be implemented according to a method
of the present invention, which is preferably implemented for a proactive
interaction between a user and a computational device through a user
interface.
The method preferably includes detecting a pattern of user behavior according
to
at least one interaction of the user with the user interface; and proactively
altering
at least one function of the user interface according to the pattern. The
pattern
includes a pattern of user preferences for the appearance, function or
characteristic of the intelligent agent.
According to another embodiment of the present invention, there is
provided a mobile information device which includes an adaptive system. Like
the user interface above, it also relies upon prior experience with a user
and/or
preprogrammed patterns. However, the adaptive system can be more restricted
to operating within the functions and environment of a mobile information
device,
such as a cellular telephone for example, which currently may also include
certain
basic functions from a PDA.
The adaptive system preferably operates with a mobile information
device featuring an operating system. The operating system can comprise an
embedded system. The mobile information device can comprise a cellular
telephone.
The adaptive system is preferably able to analyze the user behavior by
analyzing a plurality of user interactions with the mobile information device,
after
which more preferably the adaptive system compares the plurality of user
interactions to at least one predetermined pattern, to see whether the
predetermined pattern is associated with altering at least one function of the
user
interface. The analysis may also include comparing the plurality of user
interactions to at least one pattern of previously detected user behavior,
wherein
the pattern of previously detected user behavior is associated with altering
at least
one function of the user interface.
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The adaptive system may be operated by the mobile information device
itself. Alternatively, if the mobile information device is connected to a
network,
the adaptive system may be operated at least partially according to commands
sent from the network to the mobile information device.
For this
implementation, data associated with at least one operation of the adaptive
system
is stored at a location other than the mobile information device, in which the
location is accessible through the network.
According to preferred embodiments of the present invention, the adaptive
system also includes a learning module for performing the analysis according
to
received input information and previously obtained knowledge. Such
knowledge may have been previously obtained from the behavior of the user,
and/or may have been communicated from another adaptive system in
communication with the adaptive system of the particular mobile information
device. The adaptive system can adapt to user behavior according to any one or
more of an AT algorithm, a machine learning algorithm, or a genetic algorithm.
According to another optional but preferred embodiment of the present
invention, there is provided one or more intelligent agents for use with a
mobile
information device over a mobile information device network, preferably
including an avatar through which the agent may communicate with the human
user. The avatar can therefore provide a user interface for interacting with
the
user. The intelligent agent can also include an agent for controlling at least
one
interaction of the mobile information device over the network.
This
embodiment may include a plurality of such avatars being connected over the
mobile information device network.
According to preferred embodiments of the present invention, at least one
characteristic of an appearance of the avatar can be altered, for example
according
to a user command. A plurality of characteristics of an appearance of avatar
can
be altered according to a predefined avatar skin. The skin can be predefined
by
the user. By "skin" it is meant that a plurality of the characteristics is
altered
together as a set, in which the set forms the skin. If this embodiment is
combined with the previous embodiment of having at least a portion of the data
related to the avatar being stored at a network-accessible location, then the
user
could move the same avatar onto different phones, and/or customize the
appearance of the avatar for different reasons, for example for special
occasions
such as a party or other celebration. Of course, these are only intended as
examples and are not meant to be limiting in any way.
According to other embodiments of the present invention, at least one
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characteristic of an appearance of the avatar can be altered according to an
automated evolutionary algorithm, for example a genetic algorithm. The
evolutionary algorithm is one non-limiting example of a method for providing
personalization of the avatar for the user. Personalization may also be
performed through direct user selection of one or more characteristics or
skins
(groups of characteristics). Such personalization is desirable at least in
part
because it enhances the emotional experience of the user with the avatar and
hence with the mobile information device.
In terms of technical implementation, the present invention is preferably
capable of operating on a limited system (in terms of memory, data processing
capacity, screen display size and resolution, and so forth) in a device which
is also
very personal to the user. For example, the device is a mobile information
device, such as a cellular telephone, which by necessity is adapted for
portability
and ease of use, and therefore may have one or more, or all, of the above
limitations. The implementation aspects of the present invention are
preferably
geared to this combination of characteristics. Therefore, in order to overcome
the limitations of the device itself while still maintaining the desirable
personalization and "personal feel" for the user, various solutions are
proposed
below. It should be noted that these solutions are examples only, and are not
meant to be limiting in any way.
EXAMPLE 1: PROACTIVE INTERFACE ¨ General
The proactive user interface of the present invention is preferably able to
control and/or be associated with any type of computational device, in order
to
actively make suggestions to the user, based upon prior experience with a
particular user and/or various preprogrammed patterns from which the
computational device could select, depending upon user behavior. These
suggestions could be made by altering the appearance of at least a portion of
the
display, for example by changing a menu or a portion thereof; providing
different
menus for display; and/or altering touch screen functionality. The suggestions
could also be made audibly.
The proactive user interface is preferably implemented for a computational
device, as previously described, which includes an operating system. The
interface can include a user interface for communicating between the user and
the
operating system. The interface is preferably able to detect at least one
pattern
of interaction of the user with the user interface, for example through
operation of
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a learning module and would therefore be able to proactively alter at least
one
function of the user interface according to the detected pattern. The
proactive
user interface can anticipate the requests of the user and thereby assist the
user in
selecting a desired function of the computational device.
This type of proactive behavior, particularly with regard to learning the
behavior and desires of the user, requires some type of learning capability on
the
part of the proactive interface. Such learning capabilities may be provided
through algorithms and methodologies which are known in the art, relating to
learning (by the software) and interactions of a software object with the
environment. Software can be said to be learning when it can improve its
actions over a period of time. Artificial Intelligence needs to demonstrate
intelligent action selection (reasoning), such that the software has the
ability to
explore its environment (its "world") and to discover action possibilities.
The
software would also have the ability to represent the world's state and its
own
internal state. The software would then be able to select an intelligent
action
(using the knowledge above) and to act.
Learning, for example by the learning module of the interface, can be
reinforced by rewards, in which the learning module is rewarded for taking
particular actions according to the state of the environment. This type of
learning actually involves training the learning module to behave in a certain
manner. If more than one behavior is allowed, then the learning process is non-
deterministic and can create different behaviors. With regard to the proactive
user interface, for example, the reward includes causing the learning module
to
detect when an offered choice leads to a user selection, as opposed to when an
offered choice causes the user to seek a different set of one or more
selections, for
example by selecting a different menu than the one offered by the proactive
user
interface. Clearly, the proactive user interface should seek to maximize the
percentage of offerings which lead to a direct user selection from that
offering, as
this shows that the interface has correctly understood the user behavior.
Figure 1 is a block diagram of an exemplary learning module according to
the present invention for reactive learning. As shown, a learning module 100
includes a Knowledge Base 102, which acts as the memory of learning module
100, by holding information gathered by the learning module 100 as a result of
interactions with the environment. Knowledge Base 102 may be stored in non-
volatile memory (not shown). Knowledge Base 102 stores information that
assists
the learning module 100 to select the appropriate action. This information can
include values such as numerical weights for an inner neural net, or a table
with
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action reward values, or any other type of information.
In order for learning module 100 to be able to receive information related
to the environment, the learning module 100 features a plurality of sensors
104.
The sensors 104 allow the learning module 100 to perceive its environment
state.
The sensors 104 are connected to the environment and output sensed values. The
values can come from the program itself (for example, position on screen,
energy
level, etc.), or from real device values (for example, battery value and
operating
state, such as a flipper state for cellular telephones in which the device can
be
activated or an incoming call answered by opening a "flipper").
Sensors 104 clearly provide valuable information; however, this
information needs to be processed before the learning module 100 can
comprehend it. Therefore, the learning module 100 also includes a perception
unit 106, for processing the current output of the sensors 104 into a uniform
representation of the world, called a "state". The state is then the input to
a
reasoning system 108, which may be described as the "brain" of learning module
100. This design supports the extension of the world state and the sensor
mechanism, as well as supporting easy porting of the system to several host
platforms (different computational devices and environments), such that the
world
state can be changed according to the device.
The reasoning system 108 processes the current state with the Knowledge
Base 102, thereby producing a decision as to which action to perform. The
reasoning system 108 receives the current state of the world, outputs the
action to
be performed, and receives feedback on the action selected. Based on the
feedback, the reasoning system 108 updates the Knowledge Base 102. This is an
iterative process in which learning module 100 learns to associate actions to
states.
According to another embodiment of the present invention, the
computational device may feature one or more biological sensors, for sensing
various types of biological information about the user, such as emotional
state,
physical state, movement, etc. This information may then be fed to the sensors
104 for assisting the perception unit 106 in a determination of the state of
the user,
and hence to determine the proper state for the device. Such biological
sensors
may include but are not limited to sensors for body temperature, heart rate,
oxygen saturation or any other type of sensor which measures biological
parameters of the user.
Figure 2 shows an exemplary embodiment of a system 200 according to
the present invention for providing the proactive user interface, again
featuring
the learning module 100. The learning module 100 is shown communicating with
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an operating system 202 of the computational device (not shown) with which the
learning module 100 is associated and/or controls and/or by which the learning
module 100 is operated. The operating system 202 controls the operation of an
interface 204 and also at least one other software application 206 (although
of
course many such software applications may optionally be present).
The user communicates through interface 204, for example by selecting a
choice from a menu. The operating system 202 enables this communication to be
received and translated into data. The learning module 100 then preferably
receives such data, and can send a command back to the operating system 202,
for example to change some aspect of the interface 204 (for example by
offering a
different menu), and/or to operate the software application 206. The user then
responds through the interface 204; from this response, the learning module
100
learns whether or not the action (command that was sent by learning module
100)
was appropriate.
Figure 3 is a block diagram showing an exemplary implementation of a
proactive user interface system 300 according to the present invention. As
shown, system 300 features a three level architecture, with an application
layer
being supported by an Al (artificial intelligence) framework, which in turn
communicates with the host platform computational device (shown as "host
platform").
The application layer features a plurality of different applications, of which
a few non-limiting examples are shown, such as a MutateApp 302, a PreviousApp
304 and a TeachingApp 306.
The MutateApp 302 is invoked in order to control and/or initiate mutations
in the system 300. As noted above, the learning module can optionally change
its behavior through directed or semi-directed evolution, for example through
genetic algorithms. The MutateApp 302 controls and/or initiates such mutations
through evolution. The embodiment of evolution is described in greater detail
below.
The PreviousApp 304 enables a prior state of the system 300, or a portion
thereof (such as the state of the learning module) to be invoked in place of
the
current state. More specifically, the PreviousApp 304 enables the user to
return to
the previous evolutionary step if the present invention is being implemented
with
an evolutionary algorithm. More generally, the system 300 is preferably
stateful
and therefore can return to a previous state, as a history of such states is
preferably maintained.
The TeachingApp 306 is only one non-limiting example of a generic
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application which may be implemented over the AT framework layer.
The AT framework layer itself contains one or more components which
enable the user interface to behave in a proactive manner. The framework can
include a DeviceWorldMapper 308, for determining the state of the
computational
device and also that of the virtual world, as well as the relationship between
the
two states. The DeviceWorldMapper 308 receives input, for example from
various events from an EventHandler 310, in order to determine the state of
the
virtual world and that of the device.
The DeviceWorldMapper 308 also communicates with an AI/ML
(machine learning) module 312 for analyzing input data. The AI/ML module 312
also determines the behavior of the system 300 in response to various stimuli,
and
also enables the system 300 to learn, for example from the response of the
user to
different types of user interface actions. The behavior of the system 300 may
also be improved according to an evolution module 314.
The embodiment of evolution is particularly preferred with regard to the
use of an intelligent agent on a mobile information device (see below for an
example), but may also be used with any proactive user interface for a
computational device. This embodiment is used when the proactive user
interface
also features or is used in combination with an avatar.
Evolution can be simulated by a set of genetic algorithms. The basis of
these algorithms is describing the properties of the proactive interface (and
particularly the avatar's appearance) in term of genes, chromosomes, and
phenotypes. The gene is a discrete property that has a level of expression for
example a leg of a certain type. The level of the expression can be the number
of
these legs.
A phenotype is the external expression of a gene; for example the leg gene
can have different phenotypes in term of leg length or size.
The gene can go though a mutation process. This process (preferably
according to a certain probability) changes one or more parameter of the gene,
thereby producing different new phenotypes.
A chromosome is a set of genes that function together. The chromosome
can hybridize (cross-breed) with the same type of chromosome from a different
creature, thus creating a new chromosome that is a combination of its genetic
parent chromosomes.
This methodology helps in creating a generic infrastructure to simulate
visual evolution (for example of the appearance of the avatar) and/or
evolution of
the behavior of the proactive user interface. These algorithms may also be
used
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for determining non-visual behavioral characteristics, such as dexterity,
stamina
and so on. The effect could result for example in a faster creature, or a more
efficient creature. These algorithms may be used for any such characteristics
that can be described according to the previously mentioned
gene/genotype/phenotype structure, such that for example behavioral genes
could
optionally determine the behavior of AT algorithms used by the present
invention.
The algorithm output preferably provides a variety of possible descendant
avatars and/or proactive user interfaces.
The genetic algorithms use a natural selection process to decide which of
the genetic children will continue as the next generation. The selection
process
can be decided by the user or can be predefined. In this way the creature can
display interesting evolutional behavior. The generic algorithm framework can
be
used to evolve genes that encode other non visual properties of the creature,
such
as goals or character.
The Evolution module 314 is a non-limiting example of the application for
managing the evolutions of the intelligent agent. The evolution module 314
supports and also preferably manages such evolution, for example through the
operation of the MutateApp 302.
Between these different AI-type applications and the EventHandler 310,
one or more different low level managers preferably support the receipt and
handling of different events, and also the performance of different actions by
the
system 300. These managers may include but are not limited to, an
ActionManager 316, a UIManager 318, a StorageManager 320 and an
ApplicationManager 322.
The ActionManager 316 is described in greater detail below, but briefly
enables the system 300 to determine which action should be taken, for example
through the operation of the AI/ML module 312.
The UIManager 318 manages the appearance and functions of the user
interface, for example by directing changes to that interface as previously
described.
The StorageManager 320 manages the storage and handling of data, for
example with regard to the knowledge base of the system 300 (not shown).
The ApplicationManager 322 handles communications with the previously
described applications in the application layer.
All of these different managers receive events from the EventHandler 310.
Within the Al framework layer, an AT infrastructure 324 supports
communication with the host platform. The host platform itself features a host
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platform interface 326, which may be provided through the operating system of
the host platform for example.
The AT infrastructure 324 can include an I/O module 328, for receiving
inputs from the host platform interface 326 and also for sending commands to
the
host platform interface 326. A screen module 330 handles the display of the
user interface on the screen of the host platform computational device. A
resources module 332 enables the system 300 to access various host platform
resources, such as data storage and so forth.
Of course, the above Figures represent only one optional configuration for
the learning module. For example, the learning module may also be represented
as a set of individual agents, in which each agent has a simple goal. The
learning
module chooses an agent to perform an action based on the current state. The
appropriate mapping between the current state and agents can also be learned
by
the learning module with reinforcement learning.
EXAMPLE 2: ADAPTIVE SYSTEM FOR MOBILE INFORMATION DEVICE
This example relates to the illustrative implementation of an adaptive
system of the present invention with a mobile information device, although it
should be understood that this implementation is preferred but optional, and
is not
intended to be limiting in any way.
The adaptive system may optionally include any of the functionality
described above in Example 1, and may also be implemented as previously
described. This Example focuses more on the actual architecture of the
adaptive
system with regard to the mobile information device operation. Also, this
Example describes an optional but preferred implementation of the creature or
avatar according to the present invention.
The next sections describe optional but preferred embodiments of specific
technical implementations of various aspects of the adaptive system according
to
the present invention. For the purpose of description only and without any
intention of being limiting, these embodiments are based upon the optional but
preferred embodiment of an adaptive system interacting with the user through
an
intelligent agent, optionally visually represented as an avatar or "creature".
Section 1: Event Driven System
This Section describes a preferred embodiment of an event driven system
according to the present invention, including but not limited to an
application
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manager, and interactions between the device itself and the system of the
present
invention as it is operated by the device.
Figure 4 is a block diagram of an exemplary adaptive system 400
according to the present invention, and interactions of the system 400 with a
mobile information device 402. Also as shown, both the system 400 and the
mobile information device 402 interact with a user 404.
The mobile information device 402 has a number of standard functions,
which are shown divided into two categories for the purpose of explanation
only:
data and mechanisms. Mechanisms may include but are not limited to such
functions as a UI (user interface) system 406 (screen, keypad or touchscreen
input,
etc); incoming and outgoing call function 408; messaging function 410 for
example for SMS; sound 412 and/or vibration 414 for alerting user 404 of an
incoming call or message, and/or alarm, etc; and storage 416.
Data may include such information as an address (telephone) book 418;
incoming or outgoing call information 420; the location of the mobile
information
device 402, shown as location 422; message information 424; cached Internet
data 426; and data related to the user 404, shown as owner data 428.
It should be noted that mobile information device 402 may include any one
or more of the above data/mechanisms, but does not necessarily need to include
all of them, and/or may include additional data/mechanisms that are not shown.
These are simply intended as non-limiting examples with regard to the mobile
information device 402, particularly for cellular telephones.
The adaptive system 400 according to the present invention preferably
interacts with the data/mechanisms of the mobile information device 402 in
order
to be able to provide an adaptive (and also preferably proactive) user
interface,
thereby increasing the ease and efficiency with which the user 404 interacts
with
the mobile information device 402.
The adaptive system 400 features logic 430, which functions in a similar
manner as the previously described learning module, and which also operates
according to the previously described AI and machine learning algorithms.
The logic 430 is able to communicate with the knowledge base 102 as
described with regard to Figure 1 (components featuring the same reference
numbers have either identical or similar functionality, unless otherwise
stated).
The information storage 432 includes data about the actions of the mobile
information device 402, user information and so forth, and preferably
supplements the data in the knowledge base 102.
Preferably, the adaptive system 400 is capable of evolution, through an
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evolution logic 434, which may optionally combine the previously described
functionality of the evolution module 314 and the MutateApp 302 of Figure 3.
The adaptive system 400 is capable of communicating directly with the
user 404 through text and/or audible language, as supported by a language
module 436.
Particularly as described with regard to the embodiment of the present
invention in Example 3 below, but also for the adaptive system 400, the user
404
may be presented with an avatar (not shown) for the user interface. If
present,
such an avatar may be created through a 3D graphics model 438 and an animation
module 440. The avatar may be personalized for the user 404, thereby providing
an enhanced emotional experience for the user 404 when interacting with the
mobile information device 402.Figure 5A shows a block diagram of an exemplary
application management system 500, which is a core infrastructure for
supporting
the adaptive system of the present invention. The system 500 may also be used
for supporting such embodiments as a teaching application, as previously
described and also as described in greater detail below. The system 500
features
an application manager 502 for managing the different types of applications
which are part of the adaptive system according to the present invention. The
application manager 502 communicates with an application interface called a
BaseApp 504, which is implemented by all applications in the system 500. Both
the application manager 502 and the BaseApp 504 communicate events through
an EventHandler 506.
The application manager 502 is responsible for managing and providing
runtime for the execution of the system applications (applications which are
part
of the system 500). The life cycle of each such application is defined in the
BaseApp 504, which allows the application manager 502 to start, pause, resume
and exit (stop) each such application. The application manager 502 manages the
runtime execution through the step method of the interface of BaseApp 504. It
should be noted that the step method is used for execution, since the system
500
is stateful, such that each step preferably corresponds (approximately) to one
or
more states. However, execution could also be based upon threads and/or any
type of execution method.
The application manager 502 receives a timer event from the mobile
information device. The mobile information device features an operating
system,
such that the timer event is received from the operating system layer. When a
timer is invoked, the application manager 502 invokes the step of the current
application being executed. The application manager 502 switches from one
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application to another application when the user activates a different
application,
for example when using the menu system.
Some non-limiting examples of the system applications are shown,
including but not limited to, a TeachingMachineApp 508, a MutateApp 510, a
GeneStudioApp 514, a TWizardApp 516, a FloatingAgentApp 518, a
TCWorldApp 522 and a HybridApp 520. These applications are also described
in greater detail below with regard to Example 3.
The MutateApp 510 is invoked in order to control and/or initiate mutations
in the adaptive system, and/or in the appearance of an avatar representing the
adaptive system as a user interface. As noted above with regard to Example 1,
the adaptive system of the present invention can change its behavior through
directed or semi-directed evolution, for example through genetic algorithms.
The MutateApp 510 controls and/or initiates such mutations.
The GeneStudioApp 514 enables the user to perform directed and/or semi-
directed mutations through one or more manual commands. For example, the
user may wish to direct the adaptive system (through the application
management
system 500) to perform a particular task sequence upon receiving a particular
input. Alternatively, the user may wish to directly change part of the
appearance
of an avatar, if present. According to the preferred embodiments of the
present
invention, these different aspects of the adaptive system are implemented by
distinct "genes", which can then be altered by the user.
The HybridApp 520 may be invoked if the user wishes to receive
information from an external source, such as the adaptive system of another
mobile information device, and to merge this information with existing
information on the user's mobile information device. For example, the user may
wish to create an avatar having a hybrid appearance with the avatar of another
mobile information device. The HybridApp 520 also provides the main control of
the user on the entire evolutionary state of the avatar. The HybridApp 520 may
be used to instruct the user on the "life" properties of with the avatar,
which may
have a name, personality, behavior and appearance.
The TeachingMachineApp 508 is an illustrative, non-limiting example of
an application which may relate to providing instruction on the use of the
device
itself, but provides instruction on a subject which is not related to the
direct
operation of the device itself. Therefore, the TeachingMachineApp 508
represents an example of an application which is provided on the mobile
information device for a purpose other than the use of the device itself.
The TCWorldApp 522 is an application which runs the intelligent agent,
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controlling both the intelligent aspects of the agent and also the graphical
display
of the creature or avatar.
The TWizardApp 516 is another type of application which provides
information to the user. It is described with regard to the Start Wizard
application in Example 4 below. Briefly, this application contains the user
preferences and configuration of the Al framework, such as the character of
the
intelligent agent, particularly with regard to the emotional system, and also
with
regard to setting goal priorities.
The FloatingAgentApp 518 controls the appearance of the user interface,
particularly with regard to the appearance of an avatar (if present). The
FloatingAgentApp 518 enables the visual display aspects of the user interface
to
be displayed independently of the display of the avatar, which may therefore
appear to "float" over the user interface, for example. The FloatingAgentApp
,
518 is the default application being operated when no other application is
running.
Figure 5B shows an exemplary sequence diagram for the operations of the
application manager according to the present invention. As shown, an
EventHandler 506 dispatches a notification of an event to the application
manager
502, as shown in arrow 1. If the event is a timer event, then the application
manager 502 invokes the step (action) of the relevant application that was
already
invoked, as shown in arrow 1.1.1. If the event is to initiate the execution of
an
application, then the application manager 502 invokes the relevant
application, as
shown in arrow 1.2.1. If a currently running application is to be paused, then
the
application manager 502 sends the pause command to the application, as shown
in arrow 1.3.1. If a previously paused application is to be resumed: then the
application manager 502 sends the resume command to the application, as shown
in arrow 1.4.1. In any case, successful execution of the step is returned to
the
application manager 502, as shown by the relevant return arrows above. The
application manager 502 then notifies the EventHandler 506 of the successful
execution, or alternatively of the failure.
These different applications are important for enabling the adaptive system
to control various aspects of the operation of the mobile information device.
However, the adaptive system also needs to be able to communicate directly
with
various mobile information device components, through the operating system of
the mobile information device. Such communication may be performed through
a communication system 600, shown with regard to Figure 6, preferably with the
action algorithms described below.
Figures 6A and 6B show an exemplary implementation of the
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infrastructure required for the adaptive system according to the present
invention
to perform one or more actions through the operating system of the mobile
information device, as well as a sequence diagram for operation of the
communication system 600. According to embodiments of the present invention,
this infrastructure is an example of a more general concept of "Al wrappers",
or
the ability to "wrap" an existing UI (user interface) system with innovative
AT and
machine learning capabilities.
The communication system 600 is capable of handling various types of
events, with a base class event 602 that communicates with the EventHandler
506
as previously described. The EventDispatcher 604 then routes the event to the
correct object within the system of the present invention. Routing is
determined
by registration of the object with the EventDispatcher 604 for a particular
event.
The EventDispatcher 604 preferably manages a registry of handlers that
implement the EventHandler 506 interface for such notification.
Specific events for which particular handlers are implemented include a
flipper event handler 606 for cellular telephones in which the device can be
activated or an incoming call answered by opening a "flipper"; when the
flipper is
opened or closed, this event occurs. Applications being operated according to
the present invention may send events to each other, which are handled by an
InterAppEvent handler 608. An event related to the evolution (change) of the
creature or avatar is handled by an EvolutionEvent handler 610. An incoming or
outgoing telephone call is handled by a CallEvent handler 612, which in turn
has
two further handlers, a CallStartedEvent handler 614 for starting a telephone
call
and a CallEndedEvent handler 616 for ending a telephone call.
An SMS event (incoming or outgoing message) is handled by an
SMSEvent handler 618. Parameters which may be included in the event
comprise parameters related to hybridization of the creature or avatar of one
mobile information device with the creature or avatar of another mobile
information device, as described in greater detail below.
Events related to operation of the keys are preferably handled by a
KeyEvent handler 620 and/or a KeyCodeEvent handler 622. For example, if the
user depresses a key on the mobile information device, the KeyEvent handler
620
preferably handles this event, which relates to incoming information for the
operation of the system according to the present invention. In the sequence
diagram, the key_event is an object from class KeyEvent, which represents the
key event message object. The KeyEvent handler 620 handles the key_event
itself, while the KeyCodeEvent handler 622 listens for input code (both input
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events are obtained through a hook into the operating system).
A BatteryEvent handler 624 handles events related to the battery, such as a
low battery, or alternatively switching from a low power consumption mode to a
high power consumption mode.
DayTimeEvent handler 626 relates to alarm, calendar or
reminder/appointment diary events.
Figure 6B is an exemplary sequence diagram, which shows how events are
handled between the mobile information device operating system or other
control
structure and the system of the present invention. In this example, the mobile
information device has an operating system, although a similar operation flow
could be implemented for devices that lack such an operating system. If
present,
the operating system handles the input and output to/from the device, and
manages the state and events which occur for the device. The sequence diagram
in Figure 6B is an abstraction for facilitating the handling of, and the
relation to,
these events.
An operating system module (os_module) 628 causes or relates to an
event; a plurality of such modules may be present, but only one is shown for
the
purposes of clarity and without intending to be limiting in any way. The
operating system module 628 is part of the operating system of the mobile
information device. The operating system module 628 sends a notification of an
event, whether received or created by operating system module 628, to a hook
630. The hook 630 is part of the system according to the present invention,
and
is used to permit communication between the operating system and the system
according to the present invention. The hook 630 listens for relevant events
from the operating system. The hook 630 is capable of interpreting the event
from
the operating system, and of constructing the event in a message which is
comprehensible to the event 602. Hook 630 also dispatches the event to the
EventDispatcher 604, which communicates with each handler for the event,
shown as the EventHandler 506 (although there may be a plurality of such
handlers). The EventDispatcher 604 then reports to the hook 630, which reports
to the operating system module 628 about the handling of the event.
Figures 7A, 7B and 7C show exemplary events, and how they are handled by
interactions between the mobile information device (through the operating
system
of the device) and the system of the present invention. It should be noted
that
some events may be handled within the system of the present invention, without
reference to the mobile information device.
Figure 7A shows an exemplary key event sequence diagram, described
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according to a mobile information device that has the DMSS operating system
infrastructure from Qualcomm Inc., for their MSM (messaging state machine)
CDMA (code division multiple access) mobile platform. This operating system
provides operating system services such as user interface service, I/O
services and
interactive input by using the telephone keys (keypad). This example shows
how an input event from a key is generated and handled by the system of the
present invention. Other events are sent to the system in almost an identical
manner, although the function of the hook 630 alters according to the
operating
system module which is sending the event; a plurality of such hooks is
present,
such that each hook has a different function with regard to interacting with
the
operating system.
As shown in Figure 7A, a ui_do_event module 700 is a component of the
operating system and is periodically invoked. When a key on the mobile device
is pressed, the user interface (UI) structure which transfers information to
the
ui _ do _event module 700 contains the value of the key. The hook 630 then
receives the key value, identifies the event as a key event (particularly if
the
ui _ do _event module 700 dispatches a global event) and generates a key event
702.
The key event 702 is then dispatched to the EventDispatcher 604. The event is
then sent to an application 704 which has requested to receive notification of
such
an event, preferably through an event handler (not shown) as previously
described.
Notification of success (or failure) in handling the event is then preferably
returned to the EventDispatcher 604 and hence to the hook 630 and the
ui _ do_ event module 700.
Figure 7B shows a second illustrative example of a sequence diagram for
handling an event; in this case, the event is passed from the system of the
present
invention to the operating system, and is related to drawing on the screen of
the
mobile information device. Information is passed through the screen access
method of the operating system, in which the screen is (typically) represented
by
a frame buffer. The frame buffer is a memory segment that is copied by using
the screen driver (driver for the screen hardware) and displayed by the
screen.
The system of the present invention produces the necessary information for
controlling drawing on the screen to the operating system.
Turning now to Figure 7B, as shown by arrow "1", the operating system
(through scrn_update_main module 710) first updates the frame buffer for the
screen. This updating may involve drawing the background for example, which
may be displayed on every part of the screen to which data is not drawn from
the
information provided by the system of the present invention. The presence of
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such a background supports the use of semi-transparent windows, which may be
used for the creature or agent as described in greater detail below.
The Scrn_update_main module 710 then sends a request for updated data
to a screen module 712, which is part of the system of the present invention
and
which features a hook for communicating with the operating system. The screen
module 712 then sends a request to each application window, shown as an
agentWindow 714, of which a plurality may be present, for updated information
about what should be drawn to the screen. If a change has occurred, such that
an
update is required, then the agentWindow 714 notifies the screen module 712
that
the update is required. The screen module 712 then asks for the location and
size of the changed portion, preferably in two separate requests (shown as
arrows
2.1.2.1 and 2.1.2.2 respectively), for which answers are sent by the
agentWindow
714.
The screen module 712 returns the information to the operating system
through the scrn_update_main 710 in the form of an updated rectangle, as
follows.
The scrn_update_main 710 responds to the notification about the presence of an
update by copying the frame buffer to a pre-buffer (process 3.1). The screen
module 712 then draws the changes for each window into the pre-buffer, shown
as arrow 3.2.1. The pre-buffer is then copied to the frame buffer and hence to
the screen (arrow 3.3).
Figure 7C shows the class architecture for the system of the present
invention for drawing on the screen. The screen module 712 and the
agentWindow 714 are both shown. The class agentWindow 714 also
communicates with three other window classes, which provide information
regarding updating (changes to) windows: BackScreenWindow 716,
BufferedWindow 718 and DirectAccessWindow 720. The BufferedWindow 718
has two further window classes with which it communicates:
TransBufferedWindow 722 and PreBufferedWindow 724.
Section 2: Action Selection System
This Section describes a preferred embodiment of an action selection
system according to the present invention, including but not limited to a
description of optional action selection according to
incentive(s)/disincentive(s),
and so forth. In order to assist in explaining how the actions of the
intelligent
agent are selected, an initial explanation is provided with regard to the
structure
of the intelligent agent, and the interactions of the intelligent agent with
the
virtual environment which is provided by the system of the present invention.
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Figure 8A describes an exemplary structure of the intelligent agent and
Figure 8B includes an exemplary sequence diagram for the operation of the
intelligent agent. As shown with regard to Figure 8A, an intelligent agent 800
includes a plurality of classes. The main class is an AICreature 802, which
includes information about the intelligent agent such as its state,
personality, goals
etc, and also information about the appearance of the creature which visually
represents the agent, such as location, color, whether it is currently visible
and so
forth.
The AICreature 802 communicates with World 804, which is the base class
for the virtual environment for the intelligent agent. The World 804 in turn
communicates with the classes which comprise the virtual environment, of which
some non-limiting examples are shown. World 804 preferably communicates
with various instances of a WorldObject 806, which represents an object that
is
found in the virtual environment and with which the intelligent agent may
interact.
The World 804 manages these different objects and also receives information
about their characteristics, including their properties such as location and
so forth.
The World 804 also manages the properties of the virtual environment itself,
such
as size, visibility and so forth. The visual representation of the WorldObject
806
may use two dimensional or three dimensional graphics, or a mixture thereof,
and
may also use other capabilities of the mobile information device, such as
sound
production and so forth.
The WorldObject 806 itself may represent an object which belongs to one
of several classes. This abstraction enables different object classes to be
added
to or removed from the virtual environment. For example, the object may be a
"ball" which for example may start as part of a menu and then be "removed" by
the creature in order to play with it, as represented by a MenuBallObject 808.
A
GoodAnimalObject 810 also communicates with the WorldObject 806; in turn,
classes such as a FoodObject 812 (representing food for the creature), a
BadAnimalObject 814 (an animal which may annoy the creature and cause them
to fight for example) and a HouseObject 816 (a house for the creature)
preferably
communicate with the GoodAnimalObject 810. The GoodAnimalObject 810
includes the functionality to be able to draw objects on the screen and so
forth,
which is why other classes and objects preferably communicate with the
GoodAnimalObject 810. Of course, many other classes and objects are possible
in this system, since other toys may optionally be provided to the creature,
for
example.
The WorldObject 806 may also relate to the state of the intelligent agent,
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for example by providing a graded input to the state. This input is graded in
the
sense that it provides an incentive to the intelligent agent or a disincentive
to the
intelligent agent; it may also have a neutral influence. The aggregation of a
plurality of such graded inputs enables the state of the intelligent agent to
be
determined. As described with regard to the sequence diagram of Figure 8B,
and also the graph search strategy and action selection strategy diagrams of
Figures 9A and 9B respectively, the graded inputs are preferably aggregated in
order to maximize the reward returned to the intelligent agent from the
virtual
environment.
These graded inputs may also include input from the user in the form of
encouraging or discouraging feedback, so that the intelligent agent has an
incentive or disincentive, respectively, to continue the behavior for which
feedback has been provided. The calculation of the world state with respect to
feedback from the user is performed as follows:
Grade = (weighting_factor * feedback_reward) + ((1-weighting_factor) *
world reward)
In which the feedback reward results from the feedback provided by the
user and the world reward is the aggregated total reward from the virtual
environment as described above; weighting_factor is a value between 0 and 1,
which indicates the weight of the user feedback as opposed to the virtual
environment (world) feedback.
Non-limiting examples of such reward for the agent's action include
positive or negative feedback on the agent's suggestion; provision of a world
object such as a ball or food to the agent; telephone usage duration; user
teaching
duration; and the like. Each of these examples can be assigned a predetermined
score, and the agent's action can be restricted or expanded according to a
corresponding accumulated score. For example, positive and negative feedback
provided by the user may be assigned positive and negative point values,
respectively; encountering an enemy or bad animal: -20 points; obtaining a
food,
toy or house object: +5 points; low battery alarm: -1 point; correct and
incorrect
answers, when the agent teaches the user: +1 point and -1 point, respectively;
inactivity for 20 minutes: -1 point; wrong dialing: -1 point; SMS use: +1
point;
and the like. The above examples may be applied in other ways.
Figure 8B shows an illustrative sequence diagram for an exemplary set of
interactions between the virtual world and the intelligent agent of the
present
invention. The sequence starts with a request from a virtual world module 818
to the AICreature 802 for an update on the status of the intelligent agent. A
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virtual world module 818 controls and manages the entire virtual environment,
including the intelligent agent itself.
The intelligent agent then considers an action to perform, as shown by
arrow 1.1.1. The action is preferably selected through a search (arrow
1.1.1.1)
through all world objects, and then recursively through all actions for each
object,
by interacting with the World 804 and the WorldObject 806. The potential
reward for each action is evaluated (arrow 1.1.1.1.1.1) and graded (arrow
1.1.1.1.1.1.2). The action with the highest reward is selected. The overall
grade for the intelligent agent is then determined and the AICreature 802
performs the selected action.
The Virtual _world 818 then updates the location and status of all objects in
the world, by communicating with the World 804 and the WorldObject 806.
The search through various potential actions may optionally be performed
according to one or more of a number of different methods. Figures 9A and 9B
show two exemplary methods for selecting an action according to the present
invention.
Figure 9A shows an exemplary method for action selection, termed herein
a rule based strategy for selecting an action. In stage 1, the status of the
virtual
environment is determined by the World state. A World Event occurs, after
which the State Handler which is appropriate for that event is invoked in
stage 2.
The State Handler preferably queries a knowledge base in stage 3. The
knowledge base may be divided into separate sections and/or separate knowledge
bases according to the State Handler which has been invoked. In stage 4, a
response is returned to the State Handler.
In stage 5, rule base validation is performed, in which the response (and
hence the suggested action which in turn brings the intelligent agent into a
specific state) is compared against the rules. If the action is not valid,
then the
process returns to stage 1. If the action is valid, then in stage 6 the action
is
generated. The priority for the action is then determined in stage 7; more
preferably, the priority is determined according to a plurality of inputs,
including
but not limited to, an action probability, an action utility and a user
preference.
In stage 8, the action is placed in a queue for the action manager. In stage
9, the
action manager retrieves the highest priority action, which is then performed
by
the intelligent agent in stage 10.
Figure 9B shows an exemplary action selection method according to a
graph search strategy. Again, in stage 1 the process begins by determining the
state of the world (virtual environment), including the state of the
intelligent agent
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and of the objects in the world. In stage 2, the intelligent agent is queried.
In
stage 3, the intelligent agent obtains a set of legal (permitted or possible)
actions
for each world object; preferably each world object is queried as shown.
The method now branches into two parts. A first part, shown on the right,
is performed for each action path. In stage 4, an action to be performed is
simulated. In stage 5, the effect of the simulation is determined for the
world,
and is preferably determined for each world object in stage 6. In stage 7, a
grade
is determined for the effect of each action.
In stage 8, the state of the objects and hence of the world is determined, as
is the overall accumulated reward of an action. In stage 9, the effect of the
action is simulated on the intelligent agent; preferably the effect between
the
intelligent agent and each world object is also considered in stage 10.
Turning now to the left branch of the method, in stage 11, all of this
information is preferably used to determine the action path with the highest
reward. In stage 12, the action is generated. In stage 13, the action priority
is
set, preferably according to the action grade or reward. In stage 14, the
action is
placed in a queue at the action manager, as in Figure 9A. In stage 15, the
action
is considered by the action manager according to priority; the highest
priority
action is selected, and is executed in stage 16.
Next, a description is provided of an exemplary action execution method
and structure. Figure 10 shows a sequence diagram of an exemplary action
execution method according to the present invention. A handler 1000 send a
goal for an action to an action module 1002 in arrow 1, which features a base
action interface. The base action interface enables the action module 1002 to
communicate with the handler 1000 and also with other objects in the system,
which are able to generate and post actions for later execution by the
intelligent
agent, shown here as a FloatingAgentApp 1006. These actions are managed by
an action manager 1004.
The action manager 1004 has two queues containing action objects. One
queue is the ready for execution queue, while the other queue is the pending
for
execution queue. The latter queue may be used for example if an action has
been generated, but the internal state of the action is pending so that the
action is
not ready for execution. When the action state matures to be ready for
execution,
the action is preferably moved to the ready for execution queue.
An application manager 1008 interacts with the FloatingAgentApp 1006
for executing an action, as shown in arrow 2. The FloatingAgentApp 1006 then
requests the next action from the action manager 1004 (arrow 2.1); the action
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itself is provided by the action module 1002 (arrow 2.2.1). Actions are
enqueued from the handler 1000 to the action manager 1004 (arrow 3). Goals
(and hence at least a part of the priority) are set for each action by
communication
between the handler 1000 and the action module 1002 (arrow 4). Arrows 5 and
6 show the harakiri () method, described in greater detail below.
As previously described, the actions are queued in priority order. The
priority is determined through querying the interface of the action module
1002
by the action manager 1004. The priority of the action is determined according
to a calculation which includes a plurality of parameters. For example, the
parameters may include the priority as derived or inferred by the generating
object, more preferably based upon the predicted probability for the success
of the
action; the persistent priority for this type of action, which is determined
according to past experience with this type of action (for example according
to
user acceptance and action success); and the goal priority, which is
determined
according to the user preferences.
One optional calculation for managing the above parameters is as follows:
P(all) = P(action probability) * ((P(persistent priority) + P(action
goal)/10))/2)
Complementary for the priority based action execution, each action
referably has a Time To Live (ttl) period; this ttl value stands for the
amount of
execution time passed between the time when the action was posted in the ready
queue and the expiration time of this action. If an action is ready but does
not
receive a priority for execution until its ttl has expired, the action manager
1004
preferably invokes the method harakiri(), which notifies the action that it
will not
be executed. Each such invocation of harakiri() preferably decreases the
priority
of the action until a threshold is reached. After this threshold has been
reached,
the persistent priority starts to increase. This model operates to handle
actions that
were proposed or executed but failed since the user aborted the action. The
persistent priority decreases by incorporating the past experience in the
action
priority calculation.
This method shows how actions that were suggested or executed adapt to
the specific user's implicit preferences in realtime.
This model is not complete without the harakiri() mechanism since if an
action persistent priority reduces, so the action does not run, it needs to be
allowed to either be removed or else possibly run again, for example if the
user
preferences change. After several executions of harakiri(), the action may
regain
the priority to run.
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The previous Sections provide infrastructure, which enables various
actions and mechanisms to be performed through the adaptive system of the
present invention. These actions and mechanisms are described in greater
detail
below.
Section 3: Emotional System
This Section describes a preferred embodiment of an emotional system
according to the present invention, including but not limited to a description
of
specific emotions and their intensity, which are combinable to form an overall
mood. The emotional system can also includes a mechanism for allowing
moods to change as well as for controlling one or more aspects of such a
change,
such as the rate of change for example.
Figures 11A-11C feature diagrams for describing an exemplary, illustrative
implementation of an emotional system according to the present invention.
Figure 11A shows an exemplary class diagram for the emotional system, while
Figures 11B and 11C show exemplary sequence diagrams for the operation of the
emotional system according to the present invention.
As shown with regard to an emotional system 1100 according to the
present invention, the goal class (goal 1102) represents an abstract goal of
the
intelligent agent. A goal is something which the intelligent agent performs as
an
action to achieve. The goal 1102 is responsible for creating emotions based on
certain events that are related to the state of the goal and its chances of
fulfillment.
The goal 1102 interacts with the AICreature 802 (previously described
with regard to Figure 8). Briefly, the intelligent agent seeks to fulfill
goals, so the
interactions between the AICreature 802 are required in order to determine
whether or not goals have been fulfilled, which in turn impact the emotional
state
of the intelligent agent.
The emotional state itself is handled by the class EmotionalState 1104,
which in turn is connected to the class Emotion 1106. The Emotion 1106 is
itself preferably connected to classes for specific emotions such as the anger
class
AngerEmotion 1108 and the joy class JoyEmotion 1110. The EmotionalState
1104 is also preferably connected to a class which determines the pattern of
behavior, the BehavioralPatternMapper 1112.
The proactive user interface creates emotions of the agent through the
emotional system when the likelihood of success (LOS) of the abstract goal of
the
intelligent agent increases or decreases and when the likelihood of failure
(LOF)
thereof increases or decreases. When LOS increases, then the hope emotion is
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generated. When LOS decreases, the despair emotion is generated. When LOF
increases, the fear emotion is preferably generated, and when LOF decreases,
then the joy emotion is generated.
Success or failure of a goal has a significant effect on the goal state and
generated emotions. When a goal fails, despair is generated, and if the
likelihood
of success was high, frustration is also generated (since expectation of
success
was high).
When a goal succeeds, joy is generated, and if expectation and
accumulated success were high, then pride is generated.
The Emotion 1106 is a structure that has two properties, which are major
and minor types. The major type describes the high level group to which the
minor emotion belongs, preferably including POSITIVE_EMOTION and
NEGATIVE EMOTION. Minor types preferably include JOY, HOPE,
GLOAT, PRIDE, LIKE, ANGER, HATE, FEAR, FRUSTRATION, DISTRESS,
DISAPPOINTMENT. Other properties of the emotion are the intensity given
when generated, and the decay policy (i.e. the rate of change of the emotion).
The next phase after emotion generation is performed by the
EmotionalState class 1104 that accumulates emotions which were generated over
time by the intelligent agent. This class represents the collection of emotion
instances that defines the current emotional state of the intelligent agent.
The
current emotional state is defined by maintaining a hierarchy of emotion
types,
which are then generalized by aggregation and correlation. For example, the
minor emotions are aggregated into a score for POSITIVE_EMOTION and a
score for NEGATIVE EMOTION; these two categories are then correlated to
GOOD/BAD MOOD, which describes the overall mood of the intelligent agent.
The EmotionalState class 1104 is queried by the intelligent agent floating
application; whenever the dominant behavior pattern changes (by emotions
generated, decayed and generalized in the previously described model), the
intelligent agent expresses its emotional state and behaves according to that
behavioral pattern. The intelligent agent can expresses its emotional state
using
one or more of the text communication engine (described in greater detail
below),
three dimensional animation, facial expressions, two dimensional animated
=
effects and sounds.
Figure 11B is an exemplary sequence diagram for generation of an
emotion by the emotional system according to the present invention. As shown,
the application manager 502 sends a step to the FloatingAgentApp 1006 in arrow
1.
The FloatingAgentApp 1006 then determines the LOF (likelihood of failure)
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by querying the goal class 1102 in arrow 1.1. The goal 1102 then determines
the
LOF; if the new LOF is greater than the previously determined LOF, fear is
preferably generated by a request to emotion class 1106 in arrow 1.1.1.1. The
fear emotion is also added to the emotional state by communication with
EmotionalState 1104 in arrow 1.1.1.2.
Next, the application manager 502 sends another step (arrow 2) to the
FloatingAgentApp 1006, which determines the LOS (likelihood of success) by
again querying the goal 1102 in arrow 2.1. The goal 1102 then determines the
LOS; if the new LOS is greater than the previously determined LOS, hope is
preferably generated by a request to emotion class 1106 in arrow 2.1.1.1. The
hope emotion is also added to the emotional state by communication with
EmotionalState 1104 in arrow 2.1.1.2.
Arrow 3 shows the application manager 502 sending another step to the
FloatingAgentApp 1006, which requests determination of emotion according to
the actual outcome of an action. If the action has failed and the last LOS was
greater than some factor, such as 0.5, which indicated that success was
expected,
then the FloatingAgentApp 1006 causes the Goal 1102 to have despair generated
by the Emotion 1106 in arrow 3.1.1.1. The despair emotion is also added to the
emotional state by communication with the EmotionalState 1104 in arrow
3.1.1.2.
Also, if the action failed (regardless of the expectation of success),
distress is
generated by the Emotion 1106 in arrow 3.1.2. The distress emotion is also
added
to the emotional state by communication with the EmotionalState 1104 in arrow
3.1.3.
Next, the application manager 502 sends another step (arrow 4) to the
FloatingAgentApp 1006, which updates emotions based on actual success by
sending a message to goal 1102 in arrow 4.1. The goal 1102 then causes joy to
preferably be generated by a request to the emotion class 1106 in arrow 4.1.1.
The joy emotion is also added to the emotional state by communication with the
EmotionalState 1104 in arrow 4.1.2.
If actual success is greater than predicted, then the goal 1102 preferably
causes pride to be generated by a request to the emotion class 1106 in arrow
4.1.3.1.
The pride emotion is also added to the emotional state by
communication with the EmotionalState 1104 in arrow 4.1.3.2.
Figure 11C is an exemplary sequence diagram for expressing an emotion
by the emotional system according to the present invention. Such expression is
governed by the user preferences. The application manager 502 initiates
emotional expression by sending a step (arrow 1) to the FloatingAgentApp 1006,
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which queries the bp_mapper 1108 as to the behavioral pattern of the
intelligent
agent in arrow 1.1.
If the dominant behavior has changed, then the
FloatingAgentApp 1006 sends a request to the bp_display 1110 to set the
behavioral pattern (arrow 1.2.1). The bp_display 1110 controls the actual
display of emotion. The FloatingAgentApp 1006 then requests an action to be
enqueued in a message to action manager 1004 (arrow 1.2.2).
The application manager 502 sends another step (arrow 2) to the
FloatingAgentApp 1006, which requests that the action be removed from the
queue (arrow 2.1) to the action manager 1004, and that the action be performed
by the bp_display 1110.
The following table shows a non-limiting example of the definition of
actions that can be taken by the agent according to the levels of reward
points
obtained by the agent.
[Table 1]
Level Basic Actions Special Actions
5 sitting 1st-type dancing, running, 2nd-type
(high) sleeping jumping, 2nd-type flying
4 sniffing 2nd-type dancing, running, jumping,
barking flying
3 being sad 2nd-type happy, thinking, running,
being happy jumping
2 thinking, 1st-type waiting, 2nd-type
angry, 2nd-type waiting
1 2nd-type sad, thinking, angry, 2nd-type
(low) waiting
In this example, the agent can perform the basic actions such as sitting,
sleeping, sniffing, barking, being sad, and being happy, irrespective of
reward
points obtained by the agent. The level of the agent increases by one
level as
the reward points increase by +200 points, whereas it decreases by one level
as
the reward points decrease by +200 points (i.e., increase by -200 reward
points).
A different number of reward points may be set as a basis for the level
change.
In case the basic level of the agent is set to the 3rd level, the agent can
take the
basic actions and additional special actions corresponding to the 3rd level,
such as
2nd-type happy, thinking, running, and jumping, as shown in Table 1. In
this
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case, if the agent obtains +200 reward points, the level of the agent becomes
the
4th level, so that the agent can take the basic actions and additional special
actions corresponding to the 4th level, such as 2nd-type dancing, running,
jumping, and flying. The action selection system described above determines
which action should be taken from among actions available at each level, while
the emotional system controls emotional expressions corresponding respectively
to the actions. In addition to such actions and emotional expressions, the
agent
performs an emotional expression based on the following communication with
the user.
Section 4: Communication with the User
This Section describes a preferred embodiment of a communication system
for communication with the user according to the present invention, including
but
not limited to textual communication, audio communication and =graphical
communication. For the purpose of description only and without any intention
of being limiting, textual communication is described as an example of these
types of communication. The communication with the user described in this
section can be used for (but is not limited to) the agent's suggestions,
provision of
the information of the user, or the agent's emotional expressions.
Figure 12A is an exemplary sequence diagram for textual communication
according to the present invention. A text engine 1200 is responsible for
generating text that is relevant to a certain event and which can be
communicated
by the intelligent agent. The text engine 1200 includes a natural language
generation of sentences or short phrases according to templates that are
predefined and contain place holders for fillers. Combining the templates and
the
fillers together enable the text engine 1200 to generate a large number of
phrases,
which are relevant to the event to which the template belongs.
This framework can be extensible for many new and/or changing events or
subjects because additional templates can also be added, as can additional
fillers.
As shown in Figure 12A, the FloatingAgentApp 1006 communicates with
the text engine 1200 by first sending a request to generate text, preferably
for a
particular event (arrow 1). The text engine 1200 selects a template,
preferably
from a plurality of templates that are suitable for this event (arrow 1.1).
The text
engine 1200 also selects a filler for the template, preferably from a
plurality of
fillers that are suitable for this event (arrow 1.2.1). The filled template is
then
returned to the FloatingAgentApp 1006.
The following provides an example of generation of text for a mood
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change event, which is that the intelligent agent is now happy, with some
exemplary, non-limiting templates and fillers. Examples of the templates are
as
follows:
Happy template 1: "%nounl is %happy_adj2"
Happy template 2: "%self f_pronoun %happy_adj1"
Examples of the fillers are as follows:
%nounl =
{"the world", "everything", "life", "this day", "the spire}
%happy_adj1 = {"happy", "joyful", "glad", "pleased", "cheerful", "in high
spirits", "blissful", "exultant", "delighted", "cheery", "jovial", "on cloud
nine" 1
%happy_adj2 = {"nice", "beautiful", "great", "happy", "joyful", "good",
"fun"}
%self f_pronoun = {"I am", "I'm", "your intelligent agent", "your agent
friend"}
Examples of some resultant text communication phrases from
combinations of templates and fillers as follows:
I'm cheerful
the spirit is joyful
I am exultant
life is beautiful
life is good
I'm pleased
I'm jovial
I am joyful
the world is joyful
I'm glad
the spirit is joyful
the spirit is happy
the world is nice
I am happy
As another non-limiting example, a missed call template could be
constructed as follows:
%user missed a call from %missed %reaction
In this example, the user's name is used for %user; the name or other
identifier (such as telephone number for example) is entered
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to %missed; %reaction is optional and is used for the reaction of the
intelligent
agent, such as expressing disappointment for example (e.g. "I'm sad").
As shown by these examples, the text engine 1200 can generate relevant
sentences for many events, from missed call events to low battery events,
making
the user's interaction with the mobile information device richer and more
understandable.
Figure 12B shows a non-limitting example of an emotional expression "I
am happy" that the agent performs in a mobile phone.
EXAMPLE 3: EVOLUTION SYSTEM FOR AN INTELLIGENT AGENT
This example describes a preferred embodiment of an evolution system
according to the present invention, including but not limited to a description
of
DNA (DeoxyriboNucleic Acid) for the creature or avatar according to a
preferred
embodiment of the present invention, and also a description of an optional
gene
studio according to the present invention. The evolution system enables the
creature or avatar to "evolve", that is, to alter at least one aspect of the
behavior
and/or appearance of the creature. This example is described as being
operative
with the intelligent agent described in example 2, but this description is for
the
purposes of illustration only and is not meant to be limiting in any way. In
other
words, the evolution system for the intelligent agent described in this
example
may be used (but not necessarily) in conjunction with the learning module and
the
action selection system described above, thereby making it possible to
implement
a system that can determine the user's preferences and actively evolve without
requesting the user's behavior.
Evolution (change) of the intelligent agent is described herein with regard
to both tangible features of the agent, which are displayed by the avatar or
creature, and non-tangible features of the agent, which affect the behavior of
the
avatar or creature.
Figure 13A shows an exemplary evolution class diagram 1800. The
genetic model described in the class diagram allows for various properties of
the
intelligent agent to be changed, including visual as well as functional
properties.
The model includes a CreatureDNA class 1802 that represents the DNA structure.
The DNA structure is a vector of available genes and can preferably be
extended
to incorporate new genes. A gene is a parameter with a range of possible
values
(i.e. genotype). The gene is interpreted by the system according to the
present
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invention, such that the expression of the data in the gene is its genotype.
For
example the head gene is located as the first gene in the DNA, and its value
is
expressed as the visual structure of the creature's head, although preferably
the
color of the head is encoded in another gene.
In order to evolve the intelligent agent to achieve a specific DNA instance
that pleases the user, the genetic model according to the present invention
implements hybrid and mutate genetic operations that modify the DNA. The
CreatureProxy class 1804 is responsible for providing an interface to the DNA
and to the genetic operations for the system classes. CreatureProxy 1804 holds
other non-genetic information about the intelligent agent (i.e. name, birth
date,
and so forth).
The EvolutionMGR class 1806 manages the evolutions of the intelligent
agent and provides an interface to the CreatureProxy 1804 of the intelligent
agent
and its genetic operations to applications.
The EvolutionEngine class 1808 listens to evolution events that may be
generated from time to time, for indicating that a certain genetic operation
should
be invoked and performed on the intelligent agent DNA. The DNA structure is
given below.
The CreatureDNA 1802 preferably listens to such evolution events from
the EvolutionEvent 1810. The following is an algorithm defining an examplory
DNA structure.
DNA structure
#ifndef CREATURE DNA
#define CREATURE DNA
#include "CreatureDefs.h"
#include "CommSerializable.h"
#define GENE COUNT 19
#define BASE COLOR GENE 8
typedef struct internal_dna
{
unsigned char gender;
unsigned char head;
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unsigned char head_color;
unsigned char head scale;
unsigned char body;
unsigned char body color;
unsigned char body scale;
unsigned char hand;
unsigned char hand color;
unsigned char hand scale;
unsigned char tail;
unsigned char tail_color;
unsigned char tail scale;
unsigned char leg;
unsigned char leg_color;
unsigned char leg scale;
unsigned char dexterity;
unsigned char efficiancy;
unsigned char interactive;
unsigned char base_color;
1 internal dna;
typedef internal_dna p_internalDna;
/**
* This class represents the Creature DNA structure.
* The DNA holds all the data about the Creature body
parts and some
* personality and functional qualities
*/
class CreatureDNA /*: public CommSerializable*/
public:
static const int gene count;
/**
* defualt constructor, DNA is initialized to zero
*/
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CreatureDNA ( ) ;
/*
* Copy constructor
* @param other - the DNA to copy
*/
CreatureDNA(const CreatureDNA &other);
/**
* Initialization function, should be called if the
constructor was not
* called.
*/
void init();
/**
* Randomizes the DNA data
*/
void randomizeDna();
/**
* The DNA actual data
*/
union f
internal dna genes;
unsigned char data[GENE_COUNT];
1;
/**
* Range of gender gene
*/
static const int GENDER RANGE;
/**
* Range of type gene
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*/
static const int TYPE RANGE;
/**
* Range of color gene
*/
static const int COLOR RANGE;
/**
* Range of scale gene
*/
static const int SCALE RANGE;
/**
* Range of character genes
*/
static const int CHARECTER RANGE;
static const int BASE COLOR RANGE;
private:
/**
* Location of scale gene in the type,color,scale
triplet
*/
static const int SCALE LOCATION;
} ;
#endif /* CREATURE DNA */
_
Intelligent agent DNA construction is preferably performed as follows.
When providing a version of a "living" mobile phone, the DNA is preferably
composed from a Gene for each Building Block of the intelligent agent. The
building block can be a visual part of the agent, preferably including color
or
scale (size of the building block), and also can include a non visual property
that
relate to the functionality and behavior of the intelligent agent. This model
of
DNA composition can be extended as more building blocks can be added and the
expression levels of each building block can increase.
The construction of an intelligent agent from the DNA structure is
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performed with respect to each gene and its value. Each gene (building block)
value (expression level) describes a different genotype expressed in the
composed
agent. The basic building blocks of the visual agent are modeled as
prototypes,
hence the amount of prototypes dictate the range of each visual gene. It is
also
possible to generate in runtime values of expressed genes not relaying on
prototypes, for example color gene expression levels can be computed as
indexes
in the host platform color table, or scale also can be computed with respect
to the
host screen size, to obtain genotypes that are independent of predefined
prototypes. The prototype models are decomposed and then a non-prototype agent
is recomposed according to the gene values of each building block.
The following example provides an illustrative non-limiting explanation of
this process. For simplicity and clarity, color and scale, and other non
visual
genes, are not included, but the same process also applies to these genes.
Without taking into consideration the gender gene, a 16 prototype and 5
building block version of DNA may optionally be given as follows:
DNA0= { [head,0:15] , [body,0:15] , [legs, 0:15] , [hands,0:15] , [tail, 0:15]
}
Each of the 5 building blocks has 16 different possible genotypes
according to the building block gene values that are derived from the number
of
prototype models. When composing the intelligent agent, the right building
block is taken according to the value of that building block in the DNA, which
is
the value of its respective gene.
For example a specific instance of the DNA scheme described above can
be:
DNA = { [3],[5],[10],[13],[0] }
The variety of possible intelligent agent compositions in this simple DNA
version is:
Vo = (16)* (16)* (16)* (16)* (16) = (16)5 = 1048576
If a base color gene for describing the general color of the intelligent
agent (i.e. green, blue, and so forth ) is added, with expression level of
possible
16 base colors, the following variety is obtained:
DNAI=
{ [head,0:15] , [body,0:15] , [legs, 0:15] , [hands,0:15] , [tail, 0:15],
[bs color,0:15]}
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The variety then becomes:
Vi = Vo * 16 = (16)6= 16777216
If an intensity gene for the base color gene (i.e from light color to dark
color) is added to this DNA version, with an expression level of possible 16
intensities of the base color, the following variety is preferably obtained:
DNA2=
{[head,0:15] , [body,0:15] , [legs, 0:15] , [hands,0:15] , [tail, 0:15],
[bs color,0 : 15] , [intensity,0: 15]}
The variety calculation is:
V2 = VI * 16 = (16)7 = 268435456
The present invention can express a variety of agent combination types as
described above without storing the information of each of the completed
combination types. According to the present invention, only with both the
information of building blocks of the combination types and the information of
a
method for combining the building blocks is it possible to make a variety of
agent
combination types as described above. Accordingly, in case the agent is used
with a portable computational device, it is possible for each of the
computational
device users to hold a substantially-unique type of agent, thanks to diversity
in the
combination methods.
On the other hand, according to another embodiment, 16 prototype and 5
building block version of the above DNA may optionally be given as follows:
DNA0={[head0,0:15] , [body0,0:15] , [legso, 0:15] , [hands0,0:15] , [tails,
0:1511, when O<Tg<Tth, and
DNA1={[head1,0:15] , [bodyi3O:15] , [legs, 0:15] , [hands1,0:15] , [Will,
0:15]}, when TgZrth,
(where "Tg" denotes a growth time of the agent and "Tth" denotes a
threshold time).
In this example, the threshold time Tth is set to 2 weeks, but may also be
set differently. The growth time Tg of the agent indicates a time period from
when the computational device user resets the agent or starts using the agent
for
the first time, to the current time. In this case, a trait expressed by the
DNA
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may be selected from a combination of first building blocks if the growth time
of
the agent is less than 2 weeks, whereas a trait expressed by the DNAI may be
selected from a combination of second building blocks if the growth time is 2
weeks or more. If the first building-block combination is set to represent the
appearance of a younger agent, and the second building-block combination is
set
to represent the appearance of a more grown-up agent, it is possible to
implement
the appearance of an agent with the same genes that automatically grows as
time
goes on. After the user starts using the agent for the first time, the growth
of the
agent occurs only with the lapse of time. In case two threshold times are set,
the
growth of the agent is composed of three steps. It is also possible to set
more
than two threshold times.
A variety of genetic operations may be performed on the DNA, as
described with regard to Figures 13B and 13C, which show a mutation sequence
diagram and a hybridization sequence diagram, respectively.
As shown in Figure 13B, the basic mutation operation randomly selects a
gene from the gene set that can be mutated, which may be the entire DNA, and
then change the value of the selected gene within that gene's possible range
(expression levels). The basic operation can be performed numerous times.
A mutate application 1812 sends a request to the EvolutionMGR 1806
(arrow 1.1) to create a mutant. The EvolutionMGR class 1806 passes this
request to the CreatureProxy 1804, for a number of mutants (this value may be
given in the function call; arrow 1.1.1).
For each such mutant, the
CreatureProxy 1804 preferably selects a random gene (arrow 1.1.1.1.1) and
changes it to a value that is still within the gene's range (arrow 1.1.1.1.2).
The
mutant(s) are then returned to the mutate application 1812, and are preferably
displayed to the user, as described in greater detail below with regard to
Example
4.
If the user approves of a mutant, then the mutate application 1812 sends a
command to replace the existing implementation of the agent with the new
mutant (arrow 2.1) to the EvolutionMGR 1806. The EvolutionMGR 1806 then
sets the DNA for the creature at the CreatureProxy 1804 (arrow 2.1.1), which
preferably then updates the history of the agent at the agent_history 1814
(arrow
2.1.1.1).
Figure 13C shows an exemplary sequence diagram for the basic hybrid
operation (or cross-over operation), which occurs when two candidate DNAs are
aligned one to the other. Both the two candidate DNAs may be obtained from
the intelligent agent system. One of the two candidate DNAs may also be
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obtained from an intelligent agent system for another mobile information
device.
For example, in the case of an intelligent agent for a networked mobile
information device in Example 5 described below, one of the two candidate
DNAs may be obtained from an intelligent agent for a second mobile information
device of a second user via a short message service (SMS).
Taking into consideration the gender gene, the above DNA may be
represented as follows:
DNA0= { [gender, 0:1], [head,0:15] , [body,0:15] , [legs, 0:15] ,
[hands,0:15] , [tail, 0:15]
The gender gene determines whether the hybrid operation is allowed.
Preferably, the hybrid operation is allowed only between different gender
genes.
However, if the gender gene is not taken into consideration, the hybrid
operation
may be allowed in any case. For the hybrid operation, one or more cross over
points located on the DNA vector are preferably selected (the cross-over
points
number can vary from 1 to the number of genes in the DNA; this number may be
randomly selected). The operation of selecting the crossover points is called
get_cut_index. At each cross over point, the value for the DNA is selected
from
one of the existing DNA values. This may be performed randomly or according
to a count called a cutting_index. The gender-gene hybrid operation is
performed by selecting one of the corresponding two genes. The result is a mix
between the two candidate DNAs. The basic hybrid operation can be performed
numerous times with numerous candidates.
As shown, a HybridApp 1816 sends a command to the EvolutionMGR
1806 to begin the process of hybridization. The process is optionally
performed
until the user approves of the hybrid agent or aborts the process. The
EvolutionMGR 1806 starts hybridization by sending a command to obtain target
DNA (arrow 2.1.1) from the CreatureProxy 1804, with a number of cross-overs
(hybridizations) to be performed. As shown, a cutting_index is maintained to
indicate when to do a cross-over between the values of the two DNAs.
The hybrid agent is returned, and if the user approves, then the current
agent is replaced with the hybrid agent, as described above with regard to the
mutant process. In the end, the history of the agent at the agent_history 1814
is
updated.
Hybridization may be performed with agent DNA that is sent from a
source external to the mobile information device, for example in a SMS
message,
through infrared, BlueTooth or the Internet, or any other source. For the
purpose
of description only and without any intention of being limiting, this process
is
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illustrated with regard to receiving such hybrid DNA through an SMS message.
The SMS message preferably contains the data for the DNA in a MIME type.
More, the system of the present invention has a hook for this MIME type, so
that
this type of SMS message is automatically parsed for hybridization without
requiring manual intervention by the user.
Figure 14 shows an exemplary sequence diagram of such a process. As
shown, User 1 sends a request to hybridize the intelligent agent of User 1
with
that of User 2 through Handset 1. User 2 can optionally approve or reject the
request through Handset 2. If User 2 approves, the hybrid operation is
performed
between the DNA from both agents on Handset 1. The result is displayed to the
requesting party (User 1), who may save this hybrid as a replacement for the
current agent. If the hybrid is used as the replacement, then User 2 receives
a
notice and saves to the hybrid to the hybrid results collection on Handset 2.
EXAMPLE 4: USER INTERACTIONS WITH THE PRESENT INVENTION
This Example is described with regard to a plurality of representative, non-
limiting, illustrative screenshots, in order to provide an optional but
preferred
embodiment of the system of the present invention as it interacts with the
user.
Figure 15 shows an exemplary screenshot of the "floating agent", which is
the creature or avatar (visual expression of the intelligent agent).
Figure 16 shows an exemplary screenshot of a menu for selecting objects
for the intelligent agent's virtual world.
Figure 17A shows the Start Wizard application, which allows the user to
configure and modify the agent settings, as well as user preferences.
Figure 17B-17F show exemplary screenshots of an initial setting mode for
an agent after the start wizard is activated, where Figure 17B shows a
screenshot
of a setting mode for selecting the type of the agent; Figure 17C for
selecting a
color thereof; Figure 17D for selecting a name thereof; Figure 17E for
selecting a
personality thereof; and Figure 17F for indicating the completion of the agent
setting.
One example of an action to be performed with the wizard is to Set
Personality, to determine settings for the emotional system of the intelligent
agent.
Here, the user can configure the creature's personality and tendencies.
The user can determine the creature's setting by pressing the right arrow
key in order to increase the level of the characteristic and in order to do
the
opposite and decrease the level of the various characteristics such as
Enthusiasm,
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Sociability, Anti_social behavior, Temper (level of patience), Melancholy,
Egoistic behavior, and so forth.
The user is also able to set User Preferences, for example to determine
how quickly to receive help. Some other non-limiting examples of these
preferences include: communication (extent to which the agent communicates);
entertain user (controls agent playing with the user); entertain self
(controls
agent playing alone); preserve_battery (extends battery life); and
transparency_level (the level of the transparency of the creature).
The user also sets User Details with the start wizard, including but not
limited to, user name, birthday (according to an optional embodiment of the
present invention, this value is important in Hybrid SMS since it will define
the
"konghup" possibility between users, which is the ability to create a hybrid
with a
favorable astrology pattern; the konghup option is built according to suitable
tables of horsocopes and dates), and gender. Here, the "konghup" (also called
"goong-hap") is a Korean word used to describe marital harmony as predicted by
a fortuneteller, and the konghup possibility can be defined as the possibility
of a
favorable astrology pattern for inter-personal relationship.
The user can also preferably set Creature Details.
Figure 18 shows an exemplary menu for performing hybridization through
the hybrid application as previously described.
Figure 19A shows an exemplary screenshot for viewing a new creature and
generating again, by pressing on the Generate button, which enables the user
to
generate a creature randomly. Figure 19B shows the resultant creature in a
screenshot with a Hybrid button: pressing on this button confirms the user's
creature selection and passes to the creature preview window.
The preview window allows the user to see the newly generated creature in
three dimensions, and optionally to animate the creature by using the
following
options:
1. Navigation UP key: Zoom In and minimizes the size of the
creature.
2. Navigation DOWN key: Zoom Out and maximizes the size of the
creature.
3. Navigation LEFT key: Switch between the "Ok" and "Back" buttons.
4. Navigation RIGHT key: Switch between the "Ok" and "Back"
buttons.
5. Ok key (OK): Confirm selection.
6. Clear key (CLR): Exit the creature preview window to Living
Mobile
Menu.
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7. End key: Exit the creature preview window to the main menu.
8. '0' key: Lighting and shading operation on the creature.
9. '1' key: Circling the creature to the left with the clock direction.
10. '2' key: Circling the creature in the 3D.
11. '3' key: Circling the creature to the right against the clock direction.
12. '5' Key: Circling the creature in the 3D.
13. '6' key: Animates the creature in many ways. Every new pressing on
this key changes the animation type.
The animations that the creature can perform include but are not limited to,
walking, sitting, smelling, flying, and jumping.
Figure 20 shows an exemplary screenshot of the hybrid history, which
enables the user to review and explore the history of the creature's changes
in the
generations. The user can see the current creature and its parents, and also
the
parents of the parents. Preferably, for every creature there can be at most 2
parents. On the other hand, if a current DNA different from a first DNA (DNA
1) is created from the first DNA (DNA1), the creation can be set to indicate
that
mutation has occurred.
Figure 21 shows an exemplary screen shot of the Gene studio, with the
DNA Sequence of the current creature. The gene studio also preferably gives .
the opportunity for the user to change and modify the agent's DNA sequence.
The agent's DNA sequence displayed on the gene studio screen is preferably
composed of a sequence of four letters A, G, C and T. The four letters
represent
the four bases constituting biological DNA. The present invention introduces
the four letters so that the user becomes more familiar with the agent DNA. In
the present invention, the four letters correspond to four numbers required to
express the quaternary numbers. For example, if A=0, G=1, C=2, and T=3, then
AA=0, AG=1, ..., and TT=15. If the agent has a DNA sequence composed of
two letters for each building block as in the above Example 3, it is possible
to
express 16 (=4x4) different genotypes for each building block. Accordingly, if
the agent has 5 building blocks, the agent can be defined as a DNA sequence
composed of 10 (=2x5) letters, and the number of possible combinations of the
agent is 165. Similarly, if the agent has 10 building blocks and a DNA
sequence
composed of 4 letters for each building block, it is possible to express 256
(=4x4x4x4) different genotypes for each building block, a DNA sequence
required to define the agent is composed of 40 (=4x10) letters, and the number
of
possible combinations of the agent is 25610. However, the present invention is
not limited to these examples.
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A description will now be given of an evolving agent system operating in
conjunction with the learning module and the action selection system. As
described above, the hybrid history or the information as to whether a
mutation is
selected is stored in the agent system. The learning module can determine
preferences or tendencies of the user on the basis of the stored information,
and
the action selection system can provide an evolution event, according to the
determined user preferences or tendencies, to the evolution class diagram.
Information as to whether the user selects the result of the performance of
the
provided evolution event is stored in the agent system, so that the stored
information is referred to when the next evolution event is provided.
EXAMPLE 5 ¨INTELLIGENT AGENT FOR A NETWORKED MOBILE
INFORMATION DEVICE
This example relates to the use of an intelligent agent on a networked
mobile information device, preferably a cellular telephone. The intelligent
agent
comprises an avatar for interacting with the user, and an agent for
interacting with
other components on the network, such as other mobile information devices,
and/or the network itself. The avatar forms the user interface (or a portion
thereof) and also has an appearance, which is more preferably three-
dimensional.
This appearance may be humanoid but may alternatively be based upon any type
of character or creature, whether real or imaginary. The agent then handles
the
communication between the avatar and the mobile information device, and/or
other components on the network, and/or other avatars on other mobile
information devices. It should also be noted that although this implementation
is
described with regard to mobile information devices such as cellular
telephones,
the avatar aspect of the implementation (or even the agent itself) may be
implemented with the adaptive system (Example 2) and/or proactive user
interface (Example 1) as previously described.
The intelligent agent of the present invention is targeted at creating
enhanced emotional experience by applying the concept of a "Living Device".
This concept includes both emphases upon the uniqueness of the intelligent
agent,
as every living creature is unique and special in appearance and behavior,
while
also providing variety, such as a variety of avatar appearances to enhance the
user's interaction with the living device. The avatar preferably has
compelling
visual properties, with suitable supplementary objects and surrounding
environment.
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The intelligent agent preferably displays intelligent decision making, with
unexpected behavior that indicates its self-existence and independent
learning.
Such independent behavior is an important aspect of the present invention, as
it
has not been previously demonstrated for any type of user interface or
interaction
for a user and a computational device of any type, and has certainly not been
used
for an intelligent agent for a mobile information device. The intelligent
agent
can also evolve with time, as all living things, displaying visual changes.
This is
one of the most important "Living Device" properties.
The evolution step initiates an emotional response from the user of surprise
and anticipation for the next evolution step.
Evolution is a visual change of the creature with respect to time. The time
frame may be set to a year for example, as this is the lifecycle of midrange
cellular telephone in the market. During the year or quarter, periodic changes
preferably occur through evolution. The evolutionary path (adaptation to the
environment) is a result of natural selection. The natural selection can be
user
driven (i.e. user decides if the next generation is better), although another
option
is a predefined natural selection process by developing some criteria for
automatic selection.
The intelligent agent may be implemented for functioning in two "worlds"
or different environments: the telephone world and the virtual creature world.
The telephone (mobile information device) world enables the intelligent agent
to
control different functions of the telephone and to suggest various function
selections to the user, as previously described. Preferably the intelligent
agent is
able to operate on the basis of one or more telephone usage processes that are
modeled for the agent to follow. Another important aspect of the telephone
world is emotional expressions that can be either graphic expressions such as
breaking the screen or free drawing or facial and text expressions one or two
relevant words for the specific case.
The virtual world is preferably a visual display and playground area, where
objects other than the avatar can be inserted and the user can observe the
avatar
learning and interacting with them. The objects that are entered into the
world can
be predefined, with different behaviors resulting from the learning process.
The
user can give rewards or disincentives and be part of the learning process. In
this respect, the intelligent agent (through the appearance of the avatar) may
act
as a type of virtual pet or companion (for example, act as a running puppy or
a
laughing person).
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Some preferred aspects of the intelligent agent include but are not limited
to, a 3D graphic infrastructure (with regard to the appearance of the avatar);
the
use of Al and machine learning mechanisms to support both adaptive and
proactive behavior; the provision of gaming capabilities; the ability to
enhance
the usability of the mobile information device and also to provide specific
user
assistance; and provision of a host platform abstraction layer. Together,
these
features provide a robust, compelling and innovative content platform to
support
a plurality of AT applications all generically defined to be running on the
mobile
information device.
The avatar also preferably has a number of important visual aspects. For
example, the outer clip size may optionally be up to 60 x 70 pixels, although
a
different resolution may be selected according to the characteristics of the
screen
display of the mobile information device. The avatar is preferably represented
as a 3D polygonal object with several colors, but in any case preferably has a
plurality of different 3D visual characteristics, such as shades, textures,
animation
support and so forth. These capabilities may be provided through previously
created visual building blocks that are stored on the mobile information
device.
The visual appearance of the avatar is preferably composed in runtime.
The avatar may start "living" after a launch wizard, taking user preferences
into account (user introduction to the living device). In addition to
evolution,
the avatar may display small visual changes that represent mutations (color
change / movement of some key vertices in a random step). Visual evolution
step is preferably performed by addition / replacement of a building block.
The
avatar can preferably move in all directions and rotate, and more is a fully
animated 3D character.
The avatar is preferably shown as floating over the mobile information
device display with the mobile information device user interface in the
background, but may also be dismissed upon a request by the user. The avatar
is
preferably able to understand the current user's normal interaction with the
mobile
information device and tries to minimize forced hiding/dismissal by the user.
According to optional but preferred embodiments of the present invention,
the avatar can be programmed to "move" on the screen in a more natural,
physically realistic manner. For example, various types of algorithms and
parameters are available which attempt to describe physically realistic
behavior
and movement for controlling the movement of robots. Examples of such
algorithms and parameters are described in "Automatic Generation of Kinematic
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Models for the Conversion of Human Motion Capture Data into Humanoid Robot
Motion", A. Ude et al., Proc. First IEEE-RAS mt. Conf Humanoid Robots
(Humanoids
2000), Cambridge, MA, USA, September 2000. This reference describes various
human
motion capture techniques, and methods for automatically translating the
captured data
into humanoid robot kinetic parameters. Briefly, both human and robotic motion
are
modeled, and the models are used for translating actual human movement data
into data
that can be used for controlling the motions of humanoid robots.
This type of reference is useful as it provides information on how to model
the
movement of the humanoid robot. Although the present invention is concerned
with
realistic movement of an avatar (virtual character being depicted three-
dimensionally),
similar models could optionally be used for the avatar as for the humanoid
robot.
Furthermore, a model could also be constructed for modeling animal movements,
thereby
permitting more realistic movement of an animal or animal-like avatar. More
generally,
the system can handle any given set of 3D character data generically.
These models could also be used to permit the movement of the avatar to
evolve,
since different parameters of the model could be altered during the
evolutionary process,
thereby changing how the avatar moves. Such models are also useful for
describing non-
deterministic movement of the avatar, and also for enabling non-deterministic
movements to evolve. Such non-deterministic behaviour also helps to maintain
the
interest of the user.
In order to implement these different functions of the avatar and/or
intelligent
agent, the intelligent agent may be constructed as described below with regard
to Figures
7A-12B, although it should be noted that these figures only represent one
exemplary
implementation and that many different implementations are possible. Again,
the
implementation of the intelligent agent may incorporate or rely upon the
implementations
described in Examples 1 and 2 above.
Figure 22 is a block diagram of an intelligent agent system 2700 according to
the
present invention. As shown, a first user 2702 controls a first mobile
information device
2704, which for the purpose of this example may be implemented as a cellular
telephone
for illustration only and without any intention of being limiting. A second
user 2706
controls a second mobile information device 2708. The first mobile information
device
2704 and the second mobile information device 2708 preferably communicate
through a
network 2710, for example through messaging.
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Each of the first mobile information device 2704 and the second mobile
information device 2708 preferably features an intelligent agent, for
interacting
with their respective users 2702 and 2706 and also for interacting with the
other
intelligent agent. Therefore, as shown, the system 2700 enables a community of
such intelligent agents to interact with each other, and/or to obtain
information for
their respective users through the network 2710, for example.
The interactions of the users 2702 and 2706 with their respective mobile
information devices 2704, 2708 preferably include the regular operation of the
mobile information device, but also add the new exciting fiinctionalities of
"living
mobile phone". These functionalities can include the intelligent agent but
also
the use of an avatar for providing a user interface and also more preferably
for
providing an enhanced user emotional experience.
The intelligent agent preferably features an "aware" and intelligent
software framework. The inner operation of such a system preferably involve
several algorithmic tools, including but not limited to AT and ML algorithms.
The system 2700 may involve interactions between multiple users as
shown. Such interactions increase the usability and enjoyment of using the
mobile
information device for the end-user.
Figure 23 shows the intelligent agent system of Figure 20 in more detail.
As shown, a first intelligent agent 2800 is able to operate according to
scenario
data 2802 (such as the previously described knowledge base) in order to be
able
to take actions, learn and make decisions as to the operation of the mobile
information device. The learning and development process of the first
intelligent agent 2800 is supported by an evolution module 2804 for evolving
as
previously described. If the first intelligent agent 2800 communicates with
the
user through an avatar, according to a preferred embodiment of the present
invention, then an animation module 2806 is used to support the appearance of
the avatar.
The first intelligent agent 2800 may also communicate through the
network (not shown) with a backend server 2808 and/or another network resource
such as a computer 2810, for example for obtaining information for the user.
The first intelligent agent 2800 may also communicate with a second
intelligent agent 2812 as shown.
Figure 24 shows a block diagram of an exemplary implementation of an
action selection system 2900 according to the present invention, which
provides
the infrastructure for enabling the intelligent agent to select an action.
The action selection system 2900 preferably features an ActionManager
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2902 (see also Figure 10 for a description), which actually executes the
action.
A BaseAction interface 2904 provides the interface for all actions executed by
the
ActionManager 2902.
Actions may use device and application capabilities denoted as an
AnimationManager 2906 and a SoundManager 2908 that are necessary to
perform the specific action. Each action aggregates the appropriate managers
for
the correct right execution.
The AnimationManager 2906 may also control a ChangeUIAction 2910,
which changes the appearance of the visual display of the user interface. In
addition or alternatively, if an avatar is used to represent the intelligent
agent to
the user, the AnimationManager 2906 may also control a
GoAwayFromObjectAction 2912 and a GoTowardObjectAction 2914, which
enables the avatar to interact with virtual objects in the virtual world of
the avatar.
Figures 25A and 25B show two exemplary, illustrative non-limiting
screenshots of the avatar according to the present invention on the screen of
the
mobile information device. Figure 25A shows an exemplary screenshot of the
user interface for adjusting the ring tone volume through an interaction with
the
avatar. Figure 25B shows an exemplary screenshot of the user interface for
receiving a message through an interaction with the avatar.
While the invention has been described with respect to a limited number of
embodiments, it will be appreciated that many variations, modifications and
other
applications of the invention may be made.
