The last few years have seen great maturation in understanding how to use computer graphics technology to portray 3D embodied characters or virtual humans. Unlike the off-line, animator-intensive methods used in the special effects industry, real-time embodied agents are expected to exist and interact with us "live." They can be represent other people or function as autonomous helpers, teammates, or tutors enabling novel interactive educational and training applications. We should be able to interact and communicate with them through modalities we already use, such as language, facial expressions, and gesture. Various aspects and issues in real-time virtual humans will be discussed, including consistent parameterizations for gesture and facial actions using movement observation principles, and the representational basis for character believability, personality, and affect. We also describe a Parameterized Action Representation (PAR) that allows an agent to act, plan, and reason about its actions or actions of others. Besides embodying the semantics of human action, the PAR is designed for building future behaviors into autonomous agents and controlling the animation parameters that portray personality, mood, and affect in an embodied agent.

This thesis presents a study of the provision of emotions for artificial agents with the ultimate aim of enhancing their autonomy, i.e. making them more flexible, robust and self-sufficient. In recent years, the importance of emotions and their assistance to cognition has been increasingly acknowledged. Emotions are no longer considered undesirable or simply useless. Their role in various aspects of human and animal cognition like perception, attention, memory, decision-making and social interaction has been recognised as essential. The importance of emotions is much more evident insocial interaction and therefore much of the emotions research done in artificial systems focuses on the expression and recognition of emotions. However, recent neurophysiological research suggests that emotions also play a crucial part in cognition itself. This thesis investigates ways in which artificial emotions can improve autonomous behaviour in the domain of a simple, but complete, solitary learning agent. For this purpose, a non-symbolic emotion model was designed and implemented. It takes the form of a recurrent artificial neural network where emotions influence the perception

The fact that emotions are considered to be essential to human reasoning suggests that they might play an important role in autonomous robots. Experimental

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.

Numerous research studies support the claim that affect plays a critical role in decisionmaking and performance as it influences cognitive processes [see e.g., Damasio, 1994; Goleman, 1995; Picard, 1997]. Despite this body of research the role and function of affect is not generally recognized by the disciplines that address the broad issues of understanding complex systems and complex behavior, especially in the presence of learning. The innovative models and theories that have been proposed to facilitate advancement in the field of human-computer interaction (HCI) tend to focus exclusively on cognitive factors. Consequently, the resulting systems are often unable to adapt to real-world situations in which affective factors play a significant role. We propose several new models for framing a dialogue leading to new insights and innovations that incorporate theories of affect into the design of (affect-sensitive) cognitive machines. 1.

Autonomy is a very important property for a robot to have, yet implementing it in a robot is far from trivial, particularly when one requires the meaning of autonomy to include self-motivation, instead of mere automaticity. The fact that emotions are considered to be essential to human reasoning and human motivation in particular, suggests that they might play an important role in robot autonomy. The purpose of the work reported here is to know if and how emotions can help a robot in achieving autonomy. Experimental work was done in a simulated robot that adapts to its environment through the use of reinforcement learning. Results suggest that emotions can be useful in dividing the task in smaller manageable problems by focusing attention on the relevant features of the task at any one time.

The paper investigates a new Patchwork model for structured population in evolutionary search, where population size may vary. This model allows better control of both population diversity and selective pressure, and its operators are local in scope. Moreover, the Patchwork model gives a signicant exibility for introducing many additional concepts, like behavioral rules for individuals. First experiments allowed us to observe some interesting patterns which emerged during evolutionary process.

Abstract. Collective decision making involves on the one hand individual mental states such as beliefs, emotions and intentions, and on the other hand interaction with others with possibly different mental states. Achieving a satisfactory common group decision on which all agree requires that such mental states are adapted to each other by social interaction. Recent developments in Social Neuroscience have revealed neural mechanisms by which such mutual adaptation can be realised. These mechanisms not only enable intentions to converge to an emerging common decision, but at the same time enable to achieve shared underlying individual beliefs and emotions. This paper presents a computational model for such processes.

Recent evidence suggests that the emotions play a crucial role in perception, learning and rational decision making. Despite arguments to the contrary, all artificial intelligent systems are, to some extent, autonomous. This research investigates how emotion can be used as the basis for autonomy. We propose the use of an emotion-based control language that maps over all layers of a computational architecture. We report on how theoretical work and both design and computational experiments with this concept are being used to direct perception, behavior selection and reasoning in cognitive agents. 1

The paper presents the outlines of a general, multidisciplinary theory of meaning based on the concept of value as a biological and social category, synthesizing ideas from biologically oriented psychology, semiotics and cybernetics. The theory distinguishes between four types of meaning systems: cue-based, association-based, icon-based and symbol-based, forming an evolutionary and epigenetic hierarchy. This hierarchy is applied to phylogenetic and ontogenetic development, pointing out significant parallels between the two, involving both continuity and discontinuity between different levels, i.e. meaning systems. The theory is finally applied to the field of developing intelligent artificial autonomous systems, showing grave limitations in existing systems and suggesting guidelines for future work.