Social identity models for automated entity interactions

One or more social interactive goals for an automated entity such as an avatar may be determined during a social interaction between the automated entity and a selected entity such as a human. Identity attributes of identity images from an identity model of the automated entity may be used to determine a set of behavioral actions the automated entity is to take for the determined goals. Paralanguage elements expressed for the automated entity via a user interface may be altered based on the determined set of behavioral actions. The automated entity may refer to a computer implemented automaton that simulates a human in the user interface of an interactive computing environment. By way of example, an avatar cybernetic goal seeking behavior may be implemented in accordance with an identity theory model.

FIELD OF INVENTION

Embodiments of the invention disclosed herein generally relate to the field of automated personalities, and more specifically, to automated entities such as avatar social interactions.

BACKGROUND

Social identity theory may explain 1) how people develop and maintain a self-identity; 2) how people develop a sense of membership and belonging in particular groups; and 3) how the mechanics of intergroup discrimination work. The social identity theory plays a role in the study of social psychology. Stated differently, a social identity theory describes how an individual's self-concept may be derived out of interaction with others. Numerous theoretical models have been developed for describing how social identity may be formed and how it may influence human behavior. By way of non-limiting example, one identity model may be based on the impingement of social structure on the self and how the resultant structure of the self-influences social behavior. Another identity model may concentrate on the interaction of the self and the social situation and how it influences behavior.

SUMMARY

An embodiment may comprise a computer system having a tangible memory device, a processor, and program instructions, stored in the tangible memory device for execution by the processor, configured to automate automated entity behavior to emulate social human behavior according to an interactive social identity image model (SIIM) to exhibit goal seeking behavior from the automated entity during a social interaction between the automated entity and a selected entity. A data store may provide the interactive social identity image model (SIIM) for the automated entity, wherein the interactive SIIM may comprise a plurality of social identity images, each social identity image may comprise a plurality of social identity image attributes (SIIAs), and wherein at least one value of the plurality of SIIAs may change with each behavior of the automated entity and with each behavior of an entity during the social interaction between the automated entity and the selected entity. The automated entity may comprise an avatar and the selected entity may comprise a human, by way of example.

In an alternate embodiment, a computer system may comprise a processor, means for determining one or more social interactive goals (SIGs) for an automated entity during a social interaction between the automated entity and an entity based on a social identity target state and a perceived identity state, and means for determining a set of behavioral actions for the automated entity based on the determined one or more SIGs and an interaction strategy. A user interface may be provided for altering paralanguage elements expressed for the automated entity based on the determined set of behavioral actions, wherein the automated entity emulates social human behaviors. The paralanguage elements may comprise a facial expression, a gesture, body language, or combinations thereof for the automated entity.

A computer-implemented aspect may comprise a processor determining one or more social interactive goals (SIGs) for an automated entity during a social interaction between the automated entity and a selected entity based on a social identity target state and a perceived identity state, determining a set of behavioral actions for the automated entity based on the determined one or more SIGs and an interaction strategy, and may alter paralanguage elements expressed for the automated entity via a user interface based on the determined set of behavioral actions, wherein the automated entity emulates social human behaviors.

DETAILED DESCRIPTION OF EMBODIMENTS

The present invention will now be described more fully hereinafter with reference to the accompanying drawings, in which embodiments are shown by way of illustration and example. While the invention may be embodied in many forms, the embodiments herein described by way of example should not be construed to be limiting as set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. Like numerals refer to like elements.

While some terms and acronyms used in the art may be used in describing certain embodiments, it will be useful to describe known and new terms herein employed to clearly describe certain embodiments. The following descriptions are presented by way of example.

Social identity image attributes (SIIA)—A collection of descriptive attributes, such as by way of non-limiting example, feelings, with values that represents the identity state of an entity, whether human or artificial.

Reflexive target social identity image (RTSII)—The SIIA and related values that describe the target identity state of an automated entity such as an avatar itself as it would like another to ascribed it, whether human or artificial.

Reflexive social identity image (RSII)—The SIIA and related values that describe the “present” or perceived identity state of an automated entity such as an avatar itself as it believes another has ascribed it, whether human or artificial.

Actual social identity image (ASH)—The SIIA and related values that represents the “present” or perceived identity state of an entity, whether human or artificial.

Target social identity image (TSII) of others—The SIIA and related assigned values that represent the social identity target state of an entity (others), whether human or artificial.

Social identity image model (SIIM)—An interactive model configured to emulate human behavior through the use of social identity images attributes (SIIA) and social identity negotiation (SIN) driven by an interaction strategy (IS) that implements a cybernetic goal seeking behavior to attain a social interaction goal (SIG). The SIIM includes a set of social identity images used by an interaction strategy (IS) to select lines of action (LOA).

Cybernetic goal seeking behavior (CGSB) Engine—The automated selection and expression of behaviors to obtain and/or maintain a social identity target state by comparing a “present” or perceived identity state with a social identity target state and using a target differential of the comparison to select and display specific behaviors that will cause changes in the “present” or perceived state that may result in minimizing the actual differences (e.g., target differential) from the social identity target state.

Cultural Interaction protocol (CIP)—A set of culturally defined behaviors exchanged at specific points in an interaction. Non-limiting examples may include behavior expressions of greetings, decision agreements, and departures. The behavior may be expressed with little variation due to context other than time and sequence appropriateness.

Interaction strategy (IS)—The decision logic, goals and rules used to select a behavior in order to attain a target social identity image. The IS may include different importance measures in the form of different numerical “weights” applied to attributes of social identity image attributes (SIIA). The IS may also include different numerical weights applied to a line of action (LOA) increasing the likelihood they may be selected for expression.

Interactive computing environment (ICE)—A computing system that may control automated avatar social interactions using a social identity image model (SIIM) in accordance with embodiments. The avatar's expressions and behavior may be presented in an audio and visual format that may be offered on any of a number of display systems. The display systems may range from small hand-held devices to larger than life-size, three-dimensional display devices. The ICE may include a system or array of filters and sensors, the sensors may include keyboards, microphones, motion sensors, proximity sensors, touch sensors, heat sensors, weapons sensors, odor sensors, etc. to detect inputs from the human and other environmental elements with which it may be interacting. The ICE may also include systems to detect inputs from other virtually realized entities such as other avatars. In addition to avatars, the ICE also manages other entities and objects in the virtual environment.

Line of action (LOA)—A collection of language and paralanguage behaviors which express a behavior or single act in a social interaction. A non-limiting example of a greeting line of action may include a smile, nodding, extending the hand for handshake and speaking “Good morning, how are you.” The LOA may include one or more of a facial expression, paralanguage, language, body language, posture, speech affectations, gestures, emotions, social actions and phrases.

Social identity negotiation (SIN)—The turn-taking exchange of behaviors between actors, whether real or avatar, where both actors may be expressing cybernetic goal seeking behavior to attain their own social interaction goals (SIGs).

Social interaction goal (SIG)—To minimize a target differential between the SIIA values of the RTSII and RSII and also between the TSII and ASII. An actor's goal is to achieve a minimum target differential between target social identity images, for the self, and for each of the other actors engaged in the interaction and perceived or actual social identity images, for the self and for each of the other actors engaged in the other interaction. Every actor in a social situation has their own version of the social interaction goal (SIG).

In an embodiment, a computing system may be configured to apply the science of identity theory to simulated entities (e.g. avatars) in a simulated environment that accurately imitates human identity goal seeking behavior. The computing system may include a behavior library database and configured to select behavior from the behavior library database to create an automated entity such as the avatar that emulates a selected entity behavior such as human behavior as it engages in interaction with other entities, including humans. While not intended to be a limitation, the automated entity herein described by way of example will be the avatar and one selected entity will be the human.

In an embodiment, a computing system may be configured to interpret and produce statements and expressions, body language, and other paralanguage elements, summarized as lines of action (LOA) during social interactions involving at least one automated entity (e.g., an avatar) in a simulation environment. The computing system collects and stores LOAs, and selects and employs the LOA in simulated human interaction. By way of non-limiting example, the computing system provides a mechanism in which a human may interact with an automated (simulated) entity or avatar, which socially reacts to the human's speech, human's facial expressions, human's body language, and the like. These reactions may include expressing language or exhibiting facial expressions, body language, and the like, all of which represent behavior. The computing system may be configured to enable automated entities or avatars to interact with each other or with humans in a simulated environment to determine, interpret, and responsively express both language and paralanguage elements (e.g., facial expressions, body language, etc.) whereby the language and paralanguage elements may be stored in the LOA.

In an embodiment, automated avatar intelligence may be implemented in accordance with a simulated interactive SIIM that may be in compliance with an identity theory and exhibits cybernetic goal seeking behavior (CGSB). The avatar may refer to a non-human persona that simulates a human in the user interface of an ICE (e.g., simulation environment). In one embodiment, the avatar, as used herein, may be considered an automated entity. The automated entity may be one created, controlled, and maintained by program instructions executing on one or more processor.

In one embodiment, expressed behaviors, used in the SIN, may comprise either or both of language and paralanguage elements. The expression of a behavior, by a human or an avatar, may cause changes in the SIIA of everyone else in the social setting.

In one embodiment, avatars in a social setting may have their own versions of the SIIAs, describing everyone in the setting.

In one embodiment, avatars in a social setting may have their own versions of the TSII for everyone in the setting. The avatar engages in SIN by choosing behaviors that produce changes in the SIIA of the RSII in the SIIM, mathematically moving these SIIA values toward their respective values of SIIAs for TSII and RTSII.

In one embodiment, avatars may maintain a SIIM that may include the SIIA of the TSII for everyone in the interaction setting.

In one embodiment, the computing system may include a list of the LOA expected to be expressed by the avatar or by a user and the changes in SIIA these LOA will create.

In one embodiment, an interaction event may be detected for the avatar and an entity. The interaction event may be compared to the database of LOA and the closest match may be determined. The changes in SIIM associated with the LOA may be applied against all ASII maintained by the avatar in its SIIM, updating all its ASII for the others and the reflexive social identity image (RSII) for itself. The resulting ASII and RSII may be compared with the SIG, RTSII and TSII for the avatar and others respectively to identify the differences. Thus, at least one social interaction goal (SIG) may be determined for the avatar, which may be to reduce this difference to zero. An interaction strategy (IS) for the avatar to achieve the at least one SIG may be determined. A set of one or more LOA to be taken in accordance with the strategy may be determined. The avatar may perform the selected LOA during a social interaction within a user interface of the ICE. The avatar may interpret an expression of its own LOA to modify its SIIM, including, but not limited to, the ASII for the others and the RSII for itself.

Illustrations and non-limiting examples presented herein focus on a training application for socially interactive avatars. The illustrations and non-limiting examples should not be construed as limited in this regard, and avatar intelligence and avatar behavior may be applicable in a myriad of applications, which may include virtualized worlds, augmented reality applications, simulations, entertainment, cinematography, and the like. Training applications may be used as an illustration, since conventionally they have been beyond the ability of computer systems to effectively and efficiently implement, which may be a shortcoming believed to be overcome.

Thus, embodiments may be configured to emulate a cultural training avatar. Such an embodiment may provide cultural training without the need for an extensive behavior tree (BT) network of behaviors and responses. Further, the system detailed herein may be adaptable across cultures and across training situations.

With reference toFIG. 1A, one embodiment is herein described by way of example as a computing system110for controlling avatar social interactions of one or more avatars114using SIIMs168, illustrated with reference toFIG. 1B. The computer system110, herein described by way of non-limiting example, includes hardware120and includes computer program instructions130that together permit the one or more avatars114to be presented within a user interface137. Each avatar114may use a SIIM168for social interactions. That is, program instructions for the avatar114may be configured to interpret social cues of an entity with which the avatar interacts. These cues may be configured to drive the avatar's behavior. Specifically, the SIIM168may be used to determine the SIG for the avatar114. Cybernetic goal seeking behavior (CGSB) engine140may be configured to convert the SIG into a series of LOAs165for the avatar114, as illustrated with reference toFIG. 1D. The set of lines of actions LOAs165may include speech but may be configured to adjust paralanguage properties or behaviors of the avatar114, such as, without limitation, facial expressions, body language, posture, speech affectations, etc. stored in a behavior library164. Additionally, cultural influences may be defined which affect social behavior and culture interactive protocols (CIP) observed by the avatar114. The avatar social behavior and interaction of avatar114dynamically changes based on sensed or perceived feedback from the entity with which the avatar114interacts. This feedback may include paralanguage communications of the entity, as well as grammar-based communications and/or actions of the entity.

As used herein, the avatar114refers to a non-human persona that simulates a human in an interactive computing environment (ICE). A human may be able to socially interact with this avatar114, as if the avatar114were another human or living entity capable of cognition. The avatar's behavior may be controlled by computer program instructions130. The avatar114may emulate cognitive functions and behavioral interactions of a human. The avatar114may be expressed within a user interface137as an electronic image. The avatar114may take on human form and may have avatar specific attributes, such as appearance, personality, dialect, knowledge, abilities, and the like. Each avatar114may be controlled by its own SIIM168. Avatars114, as used herein, are not limited to human form, and may include animals, animated objects, such as humanized inanimate objects that are, by way of non-limiting example, included in animated movies. At least one SIIM168may be saved for avatar114for subsequent recall restarting an interaction strategy with an entity previously engaged. The identify library166may store the last state of the SIIM168for each interaction with different entities. Furthermore, the SIIMs168for multiple avatars and entities may be stored.

Paralanguage may refer to non-verbal elements of communication used to modify meaning and to convey emotion. Paralanguage may include, but not limited to, vocally-produced sounds as well as non-acoustic communications. Non-acoustic communications may include, but not limited to, body language, facial expressions, gestures, and the like. Acoustic paralanguage elements may include, but not limited to, speech affectations, such as pitch, volume, and intonation. In one embodiment, paralanguage expressions are used during social interactions as grammatically expressed content (e.g., use of normal language or grammatically expressed communications).

With reference again toFIG. 1A, the computing system110represents one possible system for carrying out the steps of method200. The computing system110may include one or more computing devices. The computing devices may be general purpose computing devices, such as personal computers, servers, in-vehicle computers, and the like. In various embodiments, computing device(s) may be implemented as stand-alone devices, as virtual devices, as distributed devices, as cooperative devices, and the like.

The hardware120comprises at least one processor122, a non-volatile memory123, a volatile memory124, a network transceiver125, a bus126, a graphics controller127, a display128, a sensory/filter array129(e.g., cameras, microphones, keyboards, mouse, biometric sensors, filters etc.), a set of data stores160, and/or other such components.

The computer program instructions130may include applications135providing a user interface137for displaying one or more avatars114. Computer program instructions130may also include a CGSB engine140containing a culture editor142, a behavior editor143, an avatar editor144, an identity engine145, and/or a behavior engine146. The behavior engine146includes a behavior classifier147.

The computer program instructions130may also include speech processor131, boot firmware132, operating system code133, a graphics library134, a set of applications135running on the computing system110.

The data stores160may include a culture library162, a behavior library164, and an identity library166. The identity library166may include data, such as the SIIM168, which provides attributes of the SIIA180, as illustrated with reference toFIG. 1C, used by the identity engine145. In one embodiment (and as expressed byFIG. 1B), each SIIM168may contain a set of social identity images (e.g., RSII, ASII, RTSII and TSII) where each social identity image includes a SIIA. Identity engine145may include programmatic code designed to permit the avatar114to emulate a sense of human identity, which may be applied (using functions of behavior engine) to permit the avatar114to engage in social interactions in a manner that emulates human social interactions in accordance with details expressed herein.

The culture library162includes a plurality of interaction strategies (IS)163A1. . .163ANwhere N is greater than one.

The set of rules used to assign values to attributes of the SIIA180and select behavior, by the behavior engine146, may be called an interaction strategy (IS), such as without limitation, the “norm of reciprocity”. A non-limiting example of the “norm of reciprocity” may include if someone gives a person something, said person owes something in return. The power of the “norm of reciprocity” has impact on a SIIM which may both manipulate, and be manipulated by it.

Another non-limiting example of an IS and its impact on a SIIM may be drawn from a social distance theory. In one embodiment, the social distance theory may include how entities understand socially defined groups, or entities defined as belonging to some classification. The social distance theory may be explained as a set of socially shared symbols (SYM)162A, and may be assigned to entities of these classifications. The set of socially shared symbols (SYM)162A may be used to create expected behaviors from entities that come from different regions of the country. People and avatars114, via the identity engine145and behavior engine146, may construct personal strategies to deal with entities from these different regions in accordance with symbols established in the identity library166. The set of socially shared symbols (SYM)162A specific to groups of entities may be quantified within data of the culture library162.

Another non-limiting example of an IS may include Role theory, wherein in Role theory behavior may be explained by the roles entities perform. In an embodiment, a IS configured for Role theory behavior may be configured to resolve as an application of identity theory whereby roles represent symbolic collections that define the role behaviors and attitudes. Social norms associated with roles may share rules about the behaviors associated with roles which may be collected into behavior sets, and stored within data of behavior library164.

Related to Role theory are the protocols that define sequences of behavior for each culture. For each culture, there is a cultural interaction protocol (CIP). Therefore, each IS may include one or more embedded CIPs CIP1. . . CIPNwhere N is a number greater than one. Each CIP may be configured as a set of culturally defined behaviors exchanged at specific time points in an interaction strategy. Non-limiting examples may include greetings, decision agreements, and departures. Each CIP may typically be expressed with little variation due to context other than time and sequence appropriateness.

People/avatar/entities may use a different IS under different conditions. Consequently, higher avatar fidelity may be implemented through multiple ISs. Additionally, an IS in use by people/avatar/entities at any point in an interaction of an IS may change. In one embodiment, the avatars114and other entities choose an interaction based on what worked in the past, socio-structural constraints, such as power differences in an interaction, and what they want to get out of the interaction strategy.

By way of non-limiting example, the IS/CIP may include one or more of a greeting strategy, departure strategy, strategy for exchanging reciprocity, role theory strategy, social distance theory strategy, elder compliance exchange strategy, child compliance exchange strategy, purchasing strategy, work life strategy, family life strategy, religious life strategy, academic life strategy, military exchange strategy, governmental dignitary exchange strategy, play exchange strategy, and sports exchange strategy.

With reference again toFIG. 1Band the SIIM168, simplification assumptions may be made to minimize the images of the SIIM168images that may need to be tracked. In one embodiment, the SIIM168may be reduced to a plurality of internal social identity images. In one embodiment, there may be four internal social identity images. The plurality of internal social identity images may include: 1) a perceived RSII174that the avatar may believe an entity has of the avatar114; 2) an ASII175of another entity or others, which may be the entity with which an avatar114interacts; 3) a RTSII176that the avatar may have of itself; and 4) an TSII177of another entity or others, which may be the target image of what the avatar “wants” the other entity to become. The RSII174may be reflexive and configured as the perception representing how the avatar114believes the entity views the avatar114. In this way, the avatar114may use other entities as a looking-glass or mirror through which it “understands” its social self.

Each of the internal social identity images of the SIIM168may include a social identity image attributes (SIIA)180. Each attribute in the SIIA180may be assigned a value. Further, weights may be assigned to the attributes of the SIIA180to bias the importance of one or more of the attributes relative to another. The weights may be situationally applied and may vary based on defined behavioral interaction strategies.

As above described,FIG. 1Cdiagrammatically illustrates one embodiment of a social identity image attributes (SIIA). The SIIA180may describe an entity in a specific relationship at a single point in time. The SIIA180may change as a social interaction proceeds. The attributes of a SIIA180will now be described by way of non-limiting example, wherein the attribute arousal181may be used to control the degree of physical reaction to the interaction at a given point in time. The attribute pleasure182may be used to control the enjoyment or lack of enjoyment in the experience. By way of a non-limiting example, a high value of arousal181and a low value of pleasure182may be used to exemplify attributes of an angry identity image.

The attribute engagement183may be used to control how attentive the entity appears to be to the interaction. The attribute affect184may be used to describe various levels of liking or disliking. Together the values or weights of these two attributes (e.g., engagement183and affect184) may be controlled to express a specific emotion. By way of non-limiting example, a high value of engagement183and a low value of affect184may also be representative of attributes of an angry identity image.

The attribute trust185may be used to control the sense of predictability expected from each other. The attribute openness186may be used to control a willingness to share. By way of a non-limiting example, both a low value of trust185and a low value of openness186may be representative of attributes of an angry identity image.

The attribute competency187may be used to control relative expected capability with regard to some aspect of the interaction. The attributes indebtedness188and commonality189may be relative values. The attribute risk190may be used to control the sense of potential loss from the interaction. By way of a non-limiting example, a high value of competency187, imbalanced indebtedness188, a low value of commonality189and a high value of risk190may be representative of attributes of an angry identity image.

The attribute relationship value191may be used to control the degree of importance associated with continued and future interactions with the other. The attribute relationship density192may be used to control the numbers and variation in relationship roles. The attribute normative control193may be used to control the degree to which behavior may be socio-structurally constrained. The attribute third person influence194may be used to control a level of localized behavioral constraint. By way of a non-limiting example, the attributes relationship value191and relationship density192combined with normative control193and third person influence194may be controlled to mitigate the performance of the angry identity or even comprise the characteristics of a righteous indignation to justify and stabilize the angry identity image.

The attribute competency187may be used to indicate or control relative expected capability with regard to some aspect of the interaction. Entities with low competency may not be allowed opportunities to perform when the risk of failure is high, for fear of loss.

The attribute of indebtedness188is a measure of the sense that one of the two or more entities in an interaction owes something to the other. It may indicate a relative level of balance or imbalance in an exchange relationship where information or objects which have perceived value are exchanged. A high sense of indebtedness may indicate an imbalanced relationship. Imbalanced relationships are uncomfortable and participants may seek to rebalance the relationship or leave the interaction to ameliorate the discomfort.

The attribute of commonality189may indicate the degree to which entities engaged in an interaction are perceived to be similar along some socially important dimension. Sharing religious belief or political opinion often indicates a high degree of commonality. On the other hand, differences in geographical or economic background may indicate a low level of commonality. This perceptual dimension is relative and situational because even individuals with many common elements such as economic and religious background may perceive a low level of commonality in the presence of some other single perceived difference. A low commonality measure can make it more difficult to reach agreement on other, unrelated matters and may cause the interaction to be shortened and closed prematurely. Entities experiencing the perception of low commonality are often uncomfortable and may desire to leave the interaction at the earliest opportunity.

The attribute risk190may be used to control the sense of potential loss from the interaction. Entities with a high value of risk may want to exit the interaction and not offer objects or information of value for fear of the costs, whatever they may be, of doing so.

Taken together, and by way of a non-limiting example, a high value of competency187, high indebtedness188, a low value of commonality189and a high value of risk190may be representative of attributes of an angry identity image. Tables 1 and 2 provide various attribute values for different expressions. The expressions may include anger, agreeable, resentment admiration, puzzlement, servitude, superiority, and compliance. The values include high, low, and not applicable (N/A).

As above described,FIG. 1Dillustrates Lines of Actions (LOAs)165in block diagram form. The LOA165includes one or more of a facial expression, paralanguage, language, body language, posture, speech affectations, gestures, emotions, social actions and phrases. During execution, one or more LOAs may be employed to achieve the desired behavior.

With reference now toFIG. 2, one method200for automating avatar social interactions in accordance with embodiments of the invention is provided by way of example. The method200begins in step205, where an interaction triggering event between an avatar and an entity may be detected. The entity interacting with the avatar may include one of a human, a human controlled entity, a machine-controlled entity (with or without machine implemented cognitive functions), an entity controlled by a group of humans, or an entity controlled by both machine-implemented cognitive functions and human choices.

A non-limiting example of an interaction triggering event may include a human initiating a two-way communication with an avatar. In a three dimensional computing space, where the avatar has a fixed position, the interaction event may be triggered by an interactive entity approaching the avatar within a threshold distance in accordance with one embodiment.

An SIIM168for the avatar may be loaded into memory in step210. In step215, avatar specific perceptual filters in the sensor/filter array129may be used to sense or receive paralanguage and other social cues from the entity, with which the avatar may interact. One or more of the social identity images of the SIIM168may be updated, as shown by step220. Updating each social identity image (e.g., RSII, ASII, RTSII and TSII) may include calculating values or weights of the attribute in the SIIA180per social identity image. A target differential, as illustrated with reference toFIG. 3, for the avatar may be determined in step225.

In step230, the system110may be configured to compute an interaction strategy (IS) in response to the target differential or SIG in light of the state/values of the SIIA180for each social identity image. In step235, a set of one or more LOAs may be determined and/or combined by the interaction strategy (IS) for given interaction protocol applicable to the interaction content. These LOA165, described earlier with reference toFIG. 1D, may be influenced by cultural interaction protocols (CIPs) applicable to the interactive context. The CIPs may vary based on cultural factors.

In step245, behaviors of the avatar may be determined, which match the LOAs to be taken. The behavior library164, described earlier with reference toFIG. 1A, may include paralanguage elements for each culture. In step250, the behaviors of the LOAs (gestures, facial expressions, body language, speech affectations, and the like) may be changed for the avatar. In one embodiment, the change may be based on the determined paralanguage elements. In step255, other programmatic actions involving the avatar may be taken. These actions may be ones that are not socially dependent, and may be not necessarily dependent on the SIIM168. If the social interaction strategy (IS) is not complete, the method may proceed from step260to step215, as illustrated with continued reference toFIG. 2. There, the avatar may gather feedback from the entity with which it interacts. This feedback may cause the avatar to dynamically adjust its social interaction strategies (IS), behavioral actions, and/or paralanguage elements.

When the social interaction is complete in step260, the avatar may be optionally placed in a state for non-active interactions, as shown by step265. By way of non-limiting example, the avatar may be placed in a fidget mode, where relatively rudimentary actions (from a behavioral perspective) may be performed. The method may end in step270.

It should be understood that although the method200may be for a two-entity social interaction, social interactions of three or more entities may be handled by the SIIM model and techniques expressed herein.

To elaborate and with reference again toFIG. 3, data flow in a CGSB engine140are illustrated by way of non-limiting example, wherein agents (entities exhibiting social interactions consistent with identity theory, which may include the avatar114as detailed herein) select behaviors, or LOA165that the avatar114expresses. The act selection function310, f( ), of the agent may be described by equation (1)
f:P*→AEq. (1)

Where f is a cybernetic goal seeking behavior, P* is a sequence of preceding perceptual experiences and A is the LOA165that is generated as a result. The realization of the act selection function310may vary from simple deterministic cause and effect to more complex functions that arise as a result of the causal interactions of the perceptual experience.

The CGSB engine140may be configured to use multiple competing goal based agent models to produce intelligence for an avatar114that appears, at least within a limited domain, to use its experience to generate speech, gestures, expressions, and/or other paralanguage elements.

The act selection function may comprise a plurality of central components. The plurality of central components may include (1) behavior classifier167that interprets and identifies inputs as behaviors and adjusts the ASII and RSII as perceived identity states322accordingly; (2) a social identity target state320, which may include the attribute values of TSII and RTSII; (3) perceived identity states322of attribute values of situationally based actual images (e.g., ASII and RSII); (4) a comparator324or mechanism that relates those situational meanings to those of the social identity target state320; and (5) a behavior engine146may be configured to select one or more LOAs in the behavior Library164to emit or output an individual behavior326(*P) or activity which emerges as a function of the difference (target differential) between perception or perceived identity state322and the social identity target state320. In an embodiment, the two or more LOAs may be combined for an overall behavior expression. The two or more LOAs may be combined to derive an overall behavior expression. The identify engine145may include or express the social identity target state320, comparator324and perceived identity state322. The identity library166includes the SIIM.

The behavior engine146may access the culture library162for the interaction strategy (IS). The IS may include at least one cultural interaction protocol (CIP) and/or symbol.

Behavior, by CGSB engine140illustrated with continued reference toFIG. 3, may be organized to emit or output behaviors that change the situation and hence the perceived self-relevant meanings in order to bring the behaviors into agreement with those in the social identity target state320. The SIIM expressed inFIG. 3(as well as other identity models) may include a sufficient definition for realization as the intelligence for avatar automation, as detailed herein.

Every behavior may be classified into how it will affect each of the attribute of the SIIA180in both the avatar and the entity the avatar interacts with, graphically illustrated with reference toFIGS. 4A and 4B. Specifically, diagram410inFIG. 4Aillustrates an interaction414between an avatar420and an entity412(which may be a person, avatar, machine-controlled entity, etc.). Grammar, actions, and paralanguage elements may be expressed during the interaction414. A perceived identity state416of the avatar420controls behavior of the avatar420during the interaction414. Specifically, the avatar420maintains a set of social identity images of itself (avatar)422and the entity's social identity image424, during the interaction, where the images may include RSII174, ASII175, RTSII176, and/or TSII177, illustrated with reference again toFIG. 1B. Each of the images422,424may have image specific attributes (e.g., SIIA180, by way of non-limiting example) illustrated with reference again toFIG. 1C. These image's attributes may be updated each time either entity412or avatar420performs a behavior.

This situation is illustrated with reference toFIG. 4Bexpressed in diagram440, which includes an interaction444between two avatars (e.g., avatar A442and avatar B450). Each avatar442,450has its own perceived identity states446,460. In one embodiment, avatar A442and avatar B450controlled by a single system (e.g., computing system110) may directly access the identity attributes of the other's identity images to simplify interaction444complexity. In another embodiment, messages may be exchanged to directly convey identity attribute values maintained for each avatar (e.g., avatar A or avatar B). In still another embodiment, the identity attributes used within the perceived identity states of each avatar (e.g., avatar A or avatar B) and the CGSB engine140may be configured to calculate image attributes of the SIIA180based on cues (e.g., expressed paralanguage elements, by way of non-limiting example) expressed (perceived) by the avatars114.

Regardless of how avatars (e.g., avatar A or avatar B) express image attributes to each other and/or update their own image attributes (per the identity engine145, by way of non-limiting example) a set of social identity images (e.g., RSII and ASH)452,454,462, and464with image attributes may be maintained for these perceived identity states446,460. Each time either avatar (e.g., avatar A or avatar B) performs a social action during the interaction444, the social identity images452,454,462and464of both avatars (e.g., avatar A or avatar B) held by both actors may be modified as a result.

Automated entities may be configured (by computer program instructions130) to choose actions configured to change these images in both entities to match the identity image being sought. This may imply that an IS that includes a TSII for both parties may be followed. By initializing the starting images of the TSII to be used by one or more avatars (e.g., avatar A or avatar B) with the values needed for a target application, starting attribute values or weights of the SIIA for the ASII and RSII may be used by the avatar (e.g., avatar A or avatar B) in its interaction with another entity.

In one embodiment, the LOA165for the behavior of the avatar114may be based on value shift functions for every possible LOA along each of the four social identity images (e.g., RSII, ASII, RTSII and TSII). In order to reduce setup and operation time, the computer program instructions130may simply associate deltas for changes in each of the attributes of the SIIA180for the social identity image in the identity library166. Computer program instructions130may provide for further simplification by assigning these deltas to sets of like LOA165, rather than each LOA165individually so long as the behaviors serve the same social function. Many CIPs may be classified this way. By way of non-limiting example, most formal greetings serve the same function and have the same social effect. Some greetings do not. Those that do, however, may receive the same delta. This may limit the number of deltas whose definitions may be required in the behavior library164. By doing so, it permits set up of general behavior classifications that may be culturally independent (e.g., independent of specifics of culture library162). The content of behavior or actions itself may be culturally specific (e.g., affected by specifics of culture library162).

Establishing behaviors, emotions, and the like to sets of image attributes180and storing these mappings in the behavior library164may require a subject matter expert (e.g., behavior psychologist, by way of non-limiting example). In one embodiment, establishing data for behavior library164may be a result of recording human reactions in a set of situations, then adjusting parameters of the behavior library164to match observations.

Once a set of social identity images of the SIIM168, states, and behavior goals have been defined, a behavior engine146may be configured to apply the behaviors to reach the goals. Specific cybernetic goal seeking behavior (CGSB) functionality may be implemented in CGSB engine140. In one embodiment, the CGSB engine140may be configured to select the behavior with the deltas that will achieve the goal. However, interaction strategy (IS) defined in the culture library162often prohibit that direct approach. By way of non-limiting example, all cultures require a greeting sequence. It may be as simple as a single “hello” statement, or more complex, involving bowing and other gestures. Additionally, most cultures also require a certain amount of ancillary conversation to precede any serious business discussion. Consequently, a larger strategy may be required to navigate goal seeking even a simple conversation.

The behavior engine146may be configured to automate the interaction behavior to navigate a “social” environment. By way of example, the behavior engine146may be configured to apply identity theory to generate interaction strategies. In one embodiment, the behavior editor143may be configured as a modeling and editing tool for modeling behaviors of avatars114. The behavior engine146may utilize the models, parameters, and settings defined by the behavior editor143. In one embodiment, the behavior editor143may utilize visualizations to graphically express strategies. Use of a graphic tool for behavior editing may permit this type of editing of behavioral characteristics to be accomplished with little direct input from a software engineer. Thus, the behavior editor143may enable a behavioral scientist to define a cross-cultural strategy, using modeling constructs known in the field of behavioral psychology.

In one embodiment, an assumption may be made that culture specific behavioral differences may be minor, which permits behavior to be generalized for any entity or avatar114. That is, behavioral characteristics expressed in the behavior library164may be generalized and cross-cultural. For situations or embodiments where cognitive processing differences between cultures may be significant due to cultural influences, culture specific behavior modifications may be accommodated.

In one embodiment, computing system110may utilize a framework that uses identity image deltas (changes), and theoretically defined strategies. System110may be considered an intelligent avatar system that may be quickly and cost effectively applied to different tasks in different cultures. That is, system110may eliminate the need to define a complicated interaction behavior tree (BT) structure that may be needed for conventional interaction systems that do not implement disclosed innovations. Thus, system110may be configured to allow the processes of the CGSB engine140and the identity image deltas (changes) to provide behavior selection for the avatar114.

With reference now toFIG. 5, one architecture540herein described by way of example provides a SIIM support structure. Two independent knowledge systems may be used, one for capturing behavior strategies (stored and maintained in behavior library164) and the other for capturing cultural data (stored and maintained in culture library162). Each knowledge system may have its own editor (e.g., culture editor142and behavior editor143), which may be graphic tools. By way of non-limiting example, the behavior editor143may be a tool optimized for behavioral scientist542. The behavioral editor143may be configured to produce the IS that applies the identity theory. In one embodiment, the strategies may be complied and executed the behavior engine146.

Office productivity products, such as spreadsheets, may be used to capture specific training information. Thus, the culture editor142may include office productivity tools/applications. Specifically, a cultural expert544may provide cultural information needed for contemplated social interactions in context of application135. Cultural data and CIPs163A1. . .163ANmay be maintained by a database (e.g., culture library162), which may be accessible by the behavior engine146.

The architecture540may provide increased affordability and reduced development time. The modular nature of architecture540, which separates application-specific functionality, behavior-specific functionality, and cultural-specific functionality, may ensure rapid development times, which may occur in parallel and ensures maximum software module reuse. That is, cultural elements from the culture library162may be re-used for a variety of applications135and interactions, which require culture-based distinctions. Similarly, the behavior library164may be reused across a vast spectrum of applications135, which conform to an identity model framework.

Returning again toFIG. 1A, the display128may include a projection system. The projection system may produce life-sized characters or avatars114, which interact with one or more users. The sensor/filter array129may include a microphone, camera, filters, proximity and motion sensors as well as computer peripherals (i.e. keyboard and mouse).

In an embodiment, the behavior engine146may include a 3D visual, 3D scene generator and simulation application. The behavior engine146may provide the artificial intelligence for cultural interactions of the avatar114and drive both the speech generation system and the avatar graphics rendering.

When a user is beyond perceptual range of the avatar, behavior engine146may assign the avatar a random “fidgeting” and model animations. This may allow creation of a virtual world expressing characters with variable levels of cognitive fidelity. In an embodiment, the system may support a large virtual training environment for high fidelity operation.

More specifically, in the specific illustration, the avatar114represents a local tribal leader who may be a point of contact for activities involving the local virtual villagers. A graphical visual database for this scenario may include 3D scene representing streets, buildings, compounds, and life forms simulating a realistic Afghanistan village environment. A behavior-dynamics graphical model rendering provides for the natural movement of the avatar114.

As a tribal leader, this avatar114has goals of personal security and support from the military presence. The avatar's conversation also supports an issue regarding the killing of a goat owned by one of his villagers. Thus, the cognitive function of the avatar114may be configured to perform a simple negotiation on behalf of this villager.

The behavior library164is configured to support the avatar114where the LOAs may include one or more facial expressions of smiling, frowning, puzzlement, and the like. Puzzlement may provide an appropriate response when unrecognized inputs may be received. The scenario may be configured to include CIP gestures like polite bow, touching the chest, and shaking the head. Movements like turning to face the user may also be included. These may be simply a few sample paralanguage elements, graphic artist and Subject Matter Experts (SMEs) may provide additional behaviors to meet additional training needs. In an embodiment, a selected LOA165may be in response to a particular CIP.

The LOAs165for avatar114may include expressions of recorded sound bites of phrases, by way of non-limiting example, Pashto phrases, may be included in addition to generated speech output. Environmental sounds may be similarly added. Visual representation of behavior associated with speaking, bobbing head, moving lips and jaw, may be also coordinated with the speech processor131, giving the avatar114realistic mannerisms.

In one embodiment, the system is configured to recognize the perceived behavior and respond to such perceived behavior. In one embodiment, the avatar may responds with puzzlement, a puzzled look, and an apology for not being able to understand the speaker.

A computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, by way of non-limiting example, in baseband or as part of a carrier wave. Such a propagated signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that may communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Aspects of various embodiments are described below with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, may be implemented by computer program instructions.