We present Cathexis, a distributed, computational model which offers an alternative approach to model the dynamic nature of different affective phenomena, such as emotions, moods and temperaments, and provides a flexible way of modeling their influence on the behavior of synthetic autonomous agents. The model has been implemented as part of an extensible, object-oriented framework which provides enough functionality for agent developers to design emotional agents that can be used in a variety of applications including entertainment (e.g. synthetic agents for interactive drama, video games, etc.), education (e.g. Intelligent Tutoring Systems), and human-computer interfaces.

People approach pleasure and avoid pain. To discover the true nature of approach-avoidance motivation, psychologists need to move beyond this hedonic principle to the principles that underlie the different ways that it operates. One such principle is regulatory focus, which distinguishes self-regulation with a promotion focus (accomplishments and aspirations)from self-regulation with a prevention focus (safety and responsibilities). This principle is used to reconsider the fundamental nature of approach-avoidance, expectancy-value relations, and emotional and evaluative sensitivities. Both types of regulatory focus are applied to phenonomena that have been treated in terms of either promotion (e.g., well-being) or prevention (e.g., cognitive dissonance). Then, regulatory focus is distinguished from regulatory anticipation and regulatory reference, 2 other principles underlying the different ways that people approach pleasure and avoid pain. It seems that our entire psychical activity is bent upon procuring pleasure and avoiding pain, that it is automatically regulated by the PLEASURE-PRINCIPLE. (Freud, 1920/1952, p. 365) People are motivated to approach pleasure and avoid pain. From the ancient Greeks, through 17th- and 18thcentury British philosophers, to 20th-century psychologists, this hedonic or pleasure principle has dominated scholars ' understanding of people's motivation. It is the basic motivational assumption of theories across all areas of psychology, including theories of emotion in psychobiology (e.g., Gray, 1982), conditioning in animal learning

This paper focuses on challenges to improving the behavioral realism of computer generated agents and attempts to reflect the state of the art in human behavior modeling with particular attention to value ontologies, emotion, and stress in game-theoretic settings. The goal is to help those interested in constructing more realistic software agents for use in simulations, in virtual reality environments, and in training and performance aiding settings such as on the web or in embedded applications. This paper pursues this goal by providing a framework for better integrating the theories and models contained in the diverse human behavior modeling literatures, such as those that straddle physiological, cognitive and emotive processes; individual differences; emergent group and crowd behavior; and (punctuated) equilibria in social settings. The framework is based on widely available ontologies of world values and how these and physiological factors might be construed emotively into subjective expected utilities to guide the reactions and deliberations of agents. For example what makes one set of opponent groups differ from another? This framework serves as an extension of Markov decision processes appropriate for iterative play in game-theoretic settings, with particular emphasis on agent capabilities for redefining drama and for finding meta-games to counter the human player. This article presents the derivation of the framework and some initial results and lessons learned about integrating behavioral models into interactive dramas and meta-games that stimulate (systemic) thought and training doctrine.

The role of emotion modeling in the development of computerized agents has long been unclear. This is partially due to instability in the philosophical issues of the problem as psychologists struggle to build models for their own purposes, and partially due to the often-wide gap between these theories and that which can be implemented by an agent author. This paper describes an effort to use emotion models in part as a deep model of utility for use in decision theoretic agents. This allows for the creation of simulated forces capable of balancing a great deal of competing goals, and in doing so they behave, for better or for worse, in a more realistic manner.

This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework. Our goal is to create a common mathematical framework (CMF) and a simulation environment that allows one to research and explore alternative behavior models to add realism to software agents -- e.g., human reaction times, constrained rationality, emotive states, and cultural influences. Our CMF is based on a dynamical, gametheoretic approach to evolution and equilibria in Markov chains representing states of the world that the agents can act upon. In these worlds the agents' utilities (payoffs) are derived by a deep model of cognitive appraisal of intention achievement including assessment of emotional activation/decay relative to concern ontologies, and subject to (integrated) stress and related constraints. We present the progress to date on the mathematical framework, and on an environment for editing the various elements of the cognitive appraiser, utility generators, concern ontologies, and Markov chains. We summarize a prototype of an example training game for counter-terrorism and crowd management. Future research needs are elaborated including validity issues and the gaps in the behavioral literatures that agent developers must struggle with.

The ability to express emotions is important for creating believable interactive characters. To simulate emotional expressions in an interactive environment, an intelligent agent needs both an adaptive model for generating believable responses, and a visualization model for mappingemotions into facial expressions. Recent advances in intelligent agents and in facial modeling have produced effective algorithms for these tasks independently. In this paper, we describe a method for integrating these algorithms to create an interactive simulation of an agent that produces appropriate facial expressions in a dynamic environment. Our approach to combining a model of emotions with a facial model represents a first step towards developing the technology of a truly believable interactive agent, which has a wide range of applications from designing intelligent training systems to video games and animation tools. 1 Introduction The ability to simulate believable characters or agents in an interac...

Neurological evidence was uncovered by A. Demasio revealing the existence of `emotional intelligence' and its importance. Following this breakthrough many computational models of emotions were developed. Although, psychological research on emotions recognized memory and experience as the main factors that define and shape the complexity of the emotional process, existing computational models of emotion did not incorporate experience or learning. We are proposing a model of an agent named PETEEI - a PET with Evolving Emotional Intelligence. PETEEI was modeled to produce emotions according to its own experience. Furthermore, PETEEI filters and expresses emotions according to its own moods and previous experience.

This paper describes an effort to integrate human behavior models from a range of ability, stress, emotion, decision theoretic, and motivation literatures into a game-theoretic framework appropriate for representing synthetic asymmetric agents and scenarios. Our goal is to create a common mathematical framework (CMF) and an open agent architecture that allows one to research and explore alternative behavior models to add realism to software agents - e.g., physiology and stress, personal values and emotive states, and cultural influences. Our CMF is based on a dynamical, game-theoretic approach to evolution and equilibria in Markov chains representing states of the world that the agents can act upon. In these worlds the agents' utilities (payoffs) are derived by a deep model of cognitive appraisal of intention achievement including assessment of emotional activation/decay relative to value hierarchies, and subject to (integrated) stress and related constraints. We present the progress to date on the mathematical framework, and on an environment for quickly editing opponents in terms of the various elements of the cognitive appraiser, utility generators, value hierarchies, and Markov chains. We illustrate the approach via an example training game for counter-terrorism and crowd management. Future research needs are elaborated including validity issues and ways to overcome the gaps in the behavioral literatures that confront developers of asymmetric forces.

Contents Abstract xiii Declaration xiv Copyright xv Declaration of honesty xvi

Abstract. Emotions play an important role in cognitive processes and specially in learning tasks. Online learning is no exception. Detecting a learner’s emotional reaction for a given situation is an essential element for every Distant Learning Environment. Nevertheless, inferring a learner’s emotional reaction in those environments is not a trivial task. In this paper, we present an agent capable of predicting a learner’s emotional reaction in a distant learning environment based on the learner’s personal and non-personal traits using a machine learning technique, namely the ID3 algorithm. We then describe the agent’s method for predicting the learner’s emotional reaction and discuss the obtained results.