Using progressive adaptability against the complexity of modeling emotionally influenced virtual agents

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1 Using progressive adaptability against the complexity of modeling emotionally influenced virtual agents Ricardo Imbert Facultad de Informática Universidad Politécnica de Madrid Angélica de Antonio Facultad de Informática Universidad Politécnica de Madrid Abstract The traditional consideration that intelligent behaviors can only be produced from pure reasoning fails when trying to explain most of human behaviors, in which the emotional component has a decisive weight. However, the inclusion of this kind of emotional factors charges agent architectures with an extra complexity, making them less efficient or reusable. This paper presents a generic cognitive architecture for agents with emotionally influenced behaviors, called COGNITIVA, which bets on adaptability to fight against that complexity. This architecture, together with a progressive specification process for its application, has been used successfully to model 3D intelligent virtual agents. Keywords: Emotion, personality, virtual characters, cognitive agent architecture, specification process 1 Including emotions in the rational process of agents 1.1 Emotion vs. rationality Emotion and rationality have been traditionally considered as two sides of the same coin and, therefore, inherently irreconcilable and not combinable. In fact, emotion has been observed as something rather irrational that plays down value to human rationality [1], something non scientific [2]. Probably, the traditional approach fails in considering emotional systems as systems that lose the desirable rationality and control. Up to this point, it is worth to remark that, from the neurological perspective, no polarization, or clean dividing line occurs between thinking and emotions [2]. 1.2 Emotional architectures for agents When trying to incorporate this emotional dimension into computer systems, most of the theoretical models are very hard to be applied directly, because their psychological formulation has a difficult fitting on computer restrictions. In fact, most of the current emotionallyinspired computational systems (almost always agent-oriented) match one of a very small group of emotional model types: appraisal models, motivational models, dimensional models... The empirical results of these approaches reveal that including an emotional influence in the agent s reasoning model helps to better explain and understand behaviors observed in real life. However, neither these models nor the architectures and systems developed from them, provide a definitive solution if there is one to the inclusion of emotions into the general process of intelligent reasoning. Some deficiency or drawback is always imputed to everyone, although, depending on the contexts and problems, they also prove sometimes to be acceptably adequate.

2 The structure underlaying emotional architectures is, frequently, very complex. Sometimes, emotional elements and mechanisms are interwoven with the restrictions and particularities of the application context and with the problem to be solved, mingling with them, and making them very difficult and costly to be reused in different contexts. (cf. [3], [4]). In other situations, emotional architectures are very generic, independent from any specific problem (cf. [5], [6]). However, usually the lack of an orientation to the particular necessities of the problem originates less-efficient, computationally demanding mechanisms. In the end, the need to produce feasible applications usually forces the designers to reconsider their structure and simplify some of their inherent features. Our hypothesis is that current solutions are not as satisfactory as they should be because they fail, precisely, in the attitude with which they cope with complexity: instead of betting on specificity or generality, we believe that the key to the solution lies in adaptivity. The complexity must be faced from a new perspective to allow the development of both reusable and efficient systems; a new approach adaptable to the specific necessities of the application context and problem, but without losing a generic nature; and a new architectural focus able to provide coherent and explainable structures, components and processes. 2 An adaptable cognitive architecture The proposal presented in this paper is a generic architecture, called COGNITIVA, meant to allow the development of agents with emotionally influenced behaviors. Considering an agent as a continuous perception-cognition-action cycle, the scope of the architecture described is restricted to the cognitive activity, although imposing no constraints on the other two modules (perceptual and actuation). As opposed to the precedent generic architectures, COGNITIVA explicitly provides mechanisms to facilitate its adaptation to any context and specific problem from a double perspective: Adaptation of its structure: COGNITIVA is a multilevel architecture that encompasses different kinds of behavior: reactive, deliberative and social. Besides, it includes a flexible model to define interdependencies and influences between elements such as personality traits, attitudes, physical states, concerns and emotions. Both behaviors and constituent elements are configurable depending on the specific needs of every individual. Adaptation through the application process: for applying the generic architecture to a particular context, a gradual specification process has been designed. This process begins with the functional specification of the generic architecture, which provides a particular design and implementation for every information structure and function described in the generic core. This first specification is, still, very context independent. In this way, a certain functional specification of the architecture will be available to be applied in many different contexts. The approaching to a concrete application problem is performed through a second specification step, called contextual specification, in which all the particular values and procedures dependent on the application environment are added. The advantage of this approach is that the core of the cognitive architecture remains independent from the context particularities. The functional specification process the more expensive, in terms of effort and complexity does not need to be repeated for any different application context, and the contextual specification process has shown to be cheap and quick. In this way, the cost of reuse is considerably reduced and the efficiency of the applications is increased. In the following sections a brief description of COGNITIVA is provided, and the specification process to be followed to model intelligent agents in a 3D virtual environment is described. 3 Description of COGNITIVA It was mentioned above that COGNITIVA is a multi-layered emotional architecture, presenting

three layers to deal with different types of behaviors: a reactive layer, to provide immediate responses to events perceived from the environment; a deliberative layer, to generate goaldriven

3 three layers to deal with different types of behaviors: a reactive layer, to provide immediate responses to events perceived from the environment; a deliberative layer, to generate goaldriven behaviors; and a social layer, to manage behaviors that take into account interactions with other agents. Besides, COGNITIVA is restricted to the cognitive function of the agent. This implies that it must interchange information with the other two agent modules (sensors and effectors). To isolate the cognitive module from the particular characteristics of sensors and effectors, the architecture proposes two specific elements: the interpreter and the scheduler, shown in figure 1. This figure represents the architecture from the perspective of its internal components and processes. Figure 1: Internal components and processes of COGNITIVA. The interpreter receives perceptions coming from the sensors, filters and discards those not interesting to the agent, and translates them into percepts 1, inteligible by the rest of the components and processes of the cognitive module. The interpreter also serves the percepts to the appropriate components and processes of the cognitive architecture, namely the beliefs, the past 1 Name proposed by Pierce [7], in the context of visual perception, to design the initial interpretative hypothesis of what is perceived. history, the processes related to the reactive behavior and the goal generators. The agent s set of beliefs represents the information that the agent maintains about the current state. COGNITIVA defines a taxonomy to manage beliefs, depending on its object and its nature. On one hand, a belief may be referred to a place in the environment, to objects located in it, and to other individuals. Besides, the agent maintains beliefs concerning the current situation, for instance, the state of a negotiation process. On the other hand, beliefs may describe defining characteristics (DCs), i.e. traits that mark out the overall features of places, objects or individuals; transitory states (TSs), characteristics that represent the current state of the environment places, objects or individuals, and whose values change more often than those of DCs; and attitudes, which influence the behavior of the agent directed towards the environment s elements (places, objects or individuals). Among the whole set of agent s beliefs, CO- GNITIVA distinguishes a small subset related to the agent itself, that has been called the agent s personal model. It includes DCs such as its personality traits, TSs such as its moods and its physical states, and also its attitudes towards others. The architecture also describes the conceptually possible relationships among these kinds of beliefs. Besides, to update moods, COGNITIVA allows the definition of expectations, inspired on the proposal of Seif El-Nasr [8], adapted, in turn, from the OCC Model [9]. Expectations capture the predisposition of the agent towards events confirmed or potential. The agent s past history maintains propositions related to any significant event from the agent s point of view that happened. Past history is a key tool to include, in the agent reasoning, considerations on events occurred in past moments. Behaviors that do not take into account past events are specially disappointing to human observers. The processes related to the agent s reactive behavior generate responses to time-demanding events produced by changes in the environment. Depending on the implicit willfulness of the response, COGNITIVA provides two different types of reactive processes: reflex processing (in-

4 stinctive pre-attentive processes) and conscious reactions processing (preconceived or learnt reactive mini-plans). The percepts coming from the interpreter, together with other factors, such as the agent s beliefs and its past history, are considered to propose new goal directed behaviors by two goal generators, a deliberative one and a social one. Depending on the scope of the goal, it could be proposed by the deliberative layer, when it is strictly circumscribed to the personal capabilities of the agent or by the social layer, when it entails some interaction with other agents. Nevertheless, the subsequent treatment of goals is independent of their origin. Two planning processes, one in the deliberative layer and another one in the social layer, collaborate to interweave actions (each one from its particular perspective), and build plans to reach every active goal. COGNITIVA considers actions proposed by the reactive layer more priority than those coming from the other two layers. To allow deliberative and social layers to control the agent s reactive behavior, the architecture provides concerns. Concerns are expressions that limit the range (a higher and a lower threshold) of desirable values for the transitory states of the personal model (moods and physical states) of the agent in a specific moment. The corresponding intermediation role between the cognitive module and the effectors is played by the scheduler. Apart from being the interface between both modules, the scheduler maintains the agent s agenda, establishing the proper order and concurrency in the execution of all the actions generated from any layer. The actions managed by the scheduler can be concrete (directly handleable by the effectors) or abstract (it must be processed before being sent to the effectors). Every time the scheduler selects an action to be executed, it will be sent to the effectors if it is a concrete action, or will be redirected to the corresponding reasoner (deliberative or social, depending on the nature of the action), if it is abstract, in order to be translated into new concrete or abstract actions. Every action to be executed will generate several expectations, that the scheduler will communicate to the interpreter to be considered in future perceptions. 4 Functional specification of the architecture The first step towards the application of the proposed architecture is the design and implementation of a functional specification. In this phase, the format of all the representations and the operation of all the functions described by the generic core of the architecture must be detailed, independently of the final application context. The first aspect to be defined is the valuation domain for every representation element proposed in COGNITIVA, and the arithmetic with which the functions specified in the architecture will manage those values. For instance, for the functional specification described in this paper, it has been chosen a fuzzy valuation of the main elements of the architecture: those of the personal model (personality traits, moods, physical states, attitudes, and upper and lower thresholds of concerns... ). Other components to be defined in any functional specification are: the possible influential functions between beliefs of the personal model; the structure and format of the past history (in the present functional specification, the mechanism designed to deal with past history is based on inverse deltas); the format of concerns (in this functional specification we have used the same fuzzy domains defined above to value both upper and lower thresholds); the structure of expectations and their associated processes (in the described functional specification, all their elements follow the fuzzy structures); the structure of actions, goals and plans, as well as the planning process that will be used by the deliberative and social layers (we have selected for this functional specification a hierarchical planner for both layers, called SHOP2 [10]); and all the functions defined in the generic architecture, including those for the management of beliefs, to update past history, to maintain the desirable value of concerns... 5 A contextual specification: the 3D Virtual Savannah A functional specification provides a context independent framework, which will serve as the basis for many different contexts, through a con-

5 text specification procedure. The advantage is that a functional specification, an effort demanding activity, will be performed only once. Along this section, the process of contextual specification is described through its application to simulating the behavior of two types of virtual characters (lions and zebras) in a virtual environment that represents an African savannah, called the 3D Virtual Savannah. Its ultimate goal is to simulate the behavior of this virtual animals, restricting their actuation to a limited set of actions, related to their movement and to some basic necessities to be satisfied, such as drinking, eating and hunting. This simple scenario has been used as a testbed for COGNITIVA. The advantage of using a cognitive architecture to control the behavior of the virtual characters is that it will be enriched with the influence of their personality traits, moods, attitudes..., producing behaviors beyond the purely rational ones. In this way, many individuals with an identical architecture, but configured with different DCs, will generate diverse behaviors, increasing the variability observed in the environment at a very low cost, and contributing to its believability for the user. The first activity proposed in the contextual specification process is the particularization of the beliefs to be maintained by both kinds of individuals (lions and zebras): an only place (the savannah); some objects (a river to quench the thirst, the pasture zone, in which zebras graze, and the lion s haunt); the beliefs about individuals (DCs about zebras e.g. their personality traits, courage and strength and lions strength ; TSs for zebras, such as their moods fear, happiness and surprise or their physical states thirst, hunger and tiredness ; TSs for lions, such as moods anger and happiness and the same set of physical states used to model zebras; and the attitudes maintained by zebras, such as the apprehension towards other individuals). In this phase are also specified: the particular relationships among beliefs of the personal model; the events that will be considered for the representation of the past history for both kinds of virtual animals; the default value of the concerns thresholds, both for zebras and lions; the actions concrete and abstract that the agents will be able to execute, the expectations about the execution of the actions; the perceptions that the interpreter will be able to receive and the set of percepts that the rest of the components of the cognitive module will be able to use; the reactive behaviors of the agents (reflex and conscious reactions); and the goals that the deliberative goals generator will manage. We have considered no social behavior needed for this specific context. The time invested in this second specification has been comparatively very small, when compared to the time devoted to the definition and implementation of the functional specification. 6 Application results The application of the progressive specification process has allow to model and implement a fully operative collection of agents with emotionally influenced behaviors. Even more, factors such as DCs make it possible to easily create many different individuals (with different behaviors) depending on their personality traits. It is viable, then, to create a herd of zebras with heterogeneous behaviors with little effort, only by varying the value of their personality traits. The motivating hypothesis was that an emotional architecture for agents have to be generic enough to cope with many different application contexts and problems, but, at the same time, it must be easily adaptable to any of those contexts and it must operate efficiently, without incurring in a disproportionate increase in effort. COGNITIVA, together with the progressive specification process defined for its application, fulfills the aims of generality and specificity, but what about the effort of adapting it to several different contexts of application? The measures recorded about the effort devoted to create the products of every specification phase, shown in figure 2, indicate that the functional specification is the most expensive activity, while the effort to generate the individuals for the specific context of the 3D Virtual Savannah has been proportionally much lower. This means that the biggest effort has to be concentrated in the development of a functional framework (the functional specification) from which many different agents will be derivable. That is, the major effort is devoted to an activity

6 References [1] D. Davis and S. Lewis. Computational models of emotion for autonomy and reasoning. Informatica (Special Edition on Perception and Emotion Based Reasoning), 27(2): , [2] R. Picard. Affective computing. Technical Report 321, MIT Media Laboratory, Perceptual Computing Section, Nov Figure 2: Distribution of the effort among the diverse phases and specification activities. that will be developed only once and that will be continuously reused. Besides, these results have been corroborated by the results obtained in the application of the same functional specification to a radically different context (the simulation of virtual bidders in an environment of Vickrey auctions). For this new contextual specification, the measures of effort have been quite similar to those obtained for the 3D Virtual Savannah. 7 Conclusions Human behaviors are rarely exclusively explainable through pure reason. Other emotional factors, that influence decisively on them, must be also considered. However, the efforts until today to build architectures including those emotional factors fail when facing the complexity through just specificity or generality. This paper presents COGNITIVA, an architecture with generic mechanisms and structures to build agents with emotionally influenced behaviors. Together with the architecture, a progressive process of specification has been proposed, to allow facing particular contexts of application without increasing its complexity or computational cost. This alternative focus on adaptability to build agents with emotionally influenced behaviors has been applied to the control of 3D virtual characters, showing to be beneficial in terms of effort, and effective in terms of believability. [3] S. Gadanho. Learning behavior-selection by emotions and cognition in a multi-goal robot task. Journal of Machine Learning Research, 4: , [4] J. Gratch and S. Marsella. Evaluating the modeling and use of emotion in virtual humans. Proceedings of the 3rd International Joint Conference on Autonomous Agents and Multi Agent Systems (AAMAS 2004), pages , New York, [5] S. Allen. Concern Processing in Autonomous Agents. PhD thesis, Faculty of Science of The University of Birmingham, School of Computer Science, UK, [6] D. Cañamero. Modeling motivations and emotions as a basis for intelligent behavior. Procs. of the First International Symposium on Autonomous Agents (Agents 97), pages , New York, [7] C. Pierce. Collected Papers. The Belknap Press of Harvard University Press, Cambridge, [8] M. Seif El-Nasr, J. Yen, and T. R. Ioerger. FLAME a fuzzy logic adaptive model of emotions. Autonomous Agents and Multi-Agent Systems, 3(3): , [9] A. Ortony, G. Clore, and A. Collins. The Cognitive Structure of Emotions. Cambridge University Press, Cambridge, UK, [10] D. Nau, T.-C. Au, O. Ilghami, U. Kuter, J. Murdock, D. Wu, and F. Yaman. SHOP2: An HTN planning system. Journal of Artificial Intelligence Research, 20: , 2003.

COGNITIVA: A Context Independent Cognitive Architecture for Agents Combining Rational and Emotional Behaviours

COGNITIVA: A Context Independent Cognitive Architecture for Agents Combining Rational and Emotional Behaviours RICARDO IMBERT, ANGÉLICA DE ANTONIO Facultad de Informática Universidad Politécnica de Madrid