Current debates regarding the possible cognitive implications of ideas from adaptive behavior research and dynamical systems theory would benefit greatly from a careful study of simple model agents that exhibit minimally cognitive behavior. This paper sketches one such agent, and presents the results of preliminary experiments on the evolution of dynamical neural networks for visually-guided orientation, object discrimination and accurate pointing with a simple manipulator to objects appearing in its field of view. 1 Introduction Many of the key ideas emphasized in adaptive behavior research are beginning to have a significant impact on cognitive science. For example, adaptive behavior research in general, and the dynamical perspective on adaptive behavior that is often taken in such research in particular, have begun to significantly influence the growing debates concerning the nature and necessity of notions of representation and computation in explaining cognitive behavio...

We report on an experiment to implement an autonomous creature situated in a two-dimensional world, that shows various learning and problem-solving capabilities, within the Society of Mind framework. This goal is approached from a developmental perspective, where phases in the experiment correspond broadly to cognitive stages in the development of an infant. This paper describes the first stage, the creature being a newborn whose behavior is strongly driven by motivational states---impulses to action based on bodily needs---and basic emotions---peripheral and cognitive responsestriggered by the recognition of a significant event. Physiological parameters are used to model both concepts, which are seen by analogy with control systems. Motivations drive behavior selection and organization based on the notions of arousal and satiation, and the exploitation principle. Emotions exert further control by sending "hormones" that may affect the intensity of the selected behavior, enable it, or p...

This paper presents a skill learning model CLARION. Different from existing models of mostly high-level skill learning that use a top-down approach (that is, turning declarative knowledge into procedural knowledge through practice), we adopt a bottom-up approach toward low-level skill learning, where procedural knowledge develops first and declarative knowledge develops later. Our model is formed by integrating connectionist, reinforcement, and symbolic learning methods to perform on-line reactive learning. It adopts a two-level dual-representation framework (Sun, 1995), with a combination of localist and distributed representation. We compare the model with human data in a minefield navigation task, demonstrating some match between the model and human data in several respects.

Emotional reasoning can be an important contribution to auto- mated tutoring and training systems. This paper describes mile, a model of emotional reasoning that builds upon existing approaches and significantly generalizes and extends their capabilities. The main contribution is to show how an explicit planning model allows a more general treatment of several stages of the reasoning process. The model supports educational applications by allowing agents to appraise the emotional significance of events as they relate to students' (or their own) plans and goals, model and predict the emotional state of others, and alter behavior accordingly.

Advances in neuroscience implicate reentrant signaling as the predominant form of communication between brain areas. This principle was used in a series of masking experiments that defy explanation by feed-forward theories. The masking occurs when a brief display of target plus mask is continued with the mask alone. Two masking processes were found: an early process affected by physical factors such as adapting luminance and a later process affected by attentional factors such as set size. This later process is called masking by object substitution, because it occurs whenever there is a mismatch between the reentrant visual representation and the ongoing lower level activity. Iterative reentrant processing was formalized in a computational model that provides an excellent fit to the data. The model provides a more comprehensive account of all forms of visual masking than do the long-held feed-forward views based on inhibitory contour interactions. From the time a stimulus first enters the eye to the time a percept emerges into consciousness, the initial stimulus has been coded at several levels in the visual system. One of the main goals in studying visual information processing is to specify the representations at each level and the temporal sequence between

In this paper, we describe algorithms developed to analyze physiological signals associated with emotions, in order to recognize the affective states of users via noninvasive technologies. We propose a framework for modeling user's emotions from the sensory inputs and interpretations of our multi-modal system. We also describe examples of circumstances that these systems can be applied to.

A basic claim of this paper is that the foundational theoretical problem of the social sciences- the possibility of unconscious, unplanned forms of cooperation and intelligence among intentional agents (the very hard issue of the ‘invisible hand’, of the ‘spontaneous social order ’ but also of ‘social functions’)- will eventually be clarified thanks to the contribution of AI (and in particular of cognitive Agent modelling, learning, and MAS) and its entering the social simulation domain. After introducing Multi-Agent-Based Social Simulation and its trends, the limits of the very popular notion of ‘emergence’ are discussed; Smith’s and Hayek’s view of ‘spontaneous social order ’ are critically introduced; and serious contradictions in the theory of ‘social functions ’ among intentional agents are pointed out. The problem is how to reconcile the ‘external’ teleology that orients the agent’s behaviour with the ‘internal ’ teleology governing it. In order to account for the functional character of intentional action, we need a somewhat sophisticated model of intention, and a different view of layered cognitive architectures combining explicit beliefs and goals with association and conditioning. On such a basis it is sketched a model of unknown functions impinging on intentional actions through a high level form of (MA)reinforcement learning. This model accounts for both eu-functions and dys-functions, autonomous and heteronomous functions. It is argued that in order to reproduce some behaviour, its effects should not necessarily be 'good ' i.e. useful for the goal of the agent or of some higher macro-system.

This paper investigates some aspects of how emotions can affect the behavior of an artificial creature, a "newborn" endowed with motivational states and basic emotions. Different "agents" integrate this creature including agents for emotional phenomena. However, contrary to cognitive approaches, we do not tackle these directly, but go below the "agent level" and use physiological parameters to model them. Motivations drive behavior selection and organization based on the notions of arousal and satiation. Emotions exert further control by sending "hormones" that may affect the creature's perceptive, attentional, and motivational mechanisms, also modifying the intensity and execution of the selected behavior.

this paper is to attempt to bring together people, results and questions from these three different disciplines -- HCI, AI, and Cognitive Science -- to explore the potential of building computer interfaces which understand and respond to the richness of the information conveyed in the human face. Until recently, information has been conveyed from the computer to the user mainly via the visual channel, whereas inputs from the user to the computer have been made from the keyboard and pointing devices via the user's motor channel. The recent emergence of multimodal interfaces as our everyday tools might restore a better balance between our physiology and sensory/motor skills, and impact (for the better we hope), the richness of activities we will find ourselves involved in. Given recent progress in user-interface primitives composed of gesture, speech, context and affect, it seems feasible to design environments which do not impose themselves as computer environments, but have a much more natural feeling associated with them.

The paper reviews the intersection of AI and VEs. It considers the use of AI as a component of a VE and Intelligent Virtual Agents as a major application area, covering movement, sensing, behaviour and control architectures. It surveys work on emotion and natural language interaction, and considers interactive narrative as a case-study. It concludes by assessing the strengths and weaknesses of the current state-of-the-art and what might take it forward.