Abstract. We describe an Affect and Belief Adaptive Interface System (ABAIS) designed to compensate for performance biases caused by users ’ affective states and active beliefs. The ABAIS architecture implements an adaptive methodology consisting of four steps: sensing/inferring user affective state and performance-relevant beliefs; identifying their potential impact on performance; selecting a compensatory strategy; and implementing this strategy in terms of speci¢c GUI adaptations. ABAIS provides a generic adaptive framework for integrating a variety of user assessment methods (e.g. knowledge-based, self-reports, diagnostic tasks, physiological sensing), and GUI adaptation strategies (e.g. content- and format-based). The ABAIS performancebias prediction isbased on empirical ¢ndings from emotion research combined with detailed knowledge of the task context.The initial ABAIS prototype was demonstrated in the context of an Air Force combat task, used a knowledge-based approach to assess the pilot’s anxiety level, and adapted to the pilot’s anxiety and belief states by modifying selected cockpit instrument displays in response to detected changes in these states.

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 adaptive value of emotions in nature indicates that they might also be useful in artificial creatures. Experiments were carried out to investigate this hypothesis in a simulated learning robot. For this purpose, a non-symbolic emotion model was developed that takes the form of a recurrent artificial neural network where emotions both depend on and influence the perception of the state of the world. This emotion

We focus on an affective aspect of communications by e-mail. In this study university students communicated their information to anonymous companions by e-mail to examine the relations between their affective traits, affective states and affective interpretations. Before the e-mail communication, as the affective traits, they were asked to give the frequencies of 12 affects that they experienced every day. Then, whenever they received the e-mails, using questionnaires, we asked them as follows: (1) what affect they produced: affective states (2) how they interpreted companions' affect: affective interpretations. By the result of the factor analysis of the data of the affective traits, the subjects divided into the three groups: group of "Negative Affects", "Positive Affects" and "Affects of Enemies". The subjects in each group were compared about the correlation between the affective interpretations and states. We found that in communications by e-mail when the subjects interpreted their companions' affective states as positive affective states, they might tend to feel positive affects. But, when they interpreted their companions' affective states as negative affects, their affective states might be influenced by their affective traits. This may demonstrate that the affective traits influence on our affective states in e-mail communications.

Most of us are taught from early on that logical, rational calculation forms the basis of sound decisions. We assume that emotions can only interfere with, and hamper, this process. But what if we were wrong? What if emotion, and not cognition, as we have traditionally understood it, assumed primary status? What if sound, rational decision making in fact depended on prior accurate emotional processing? What if emotion, and not cognition, provided the foundation for swift and accurate decision making? Recent advances in the neurosciences have begun to suggest just that. Recent findings in the last decade in the neurosciences offer a wealth of new information about how the brain works, and how the body and mind interact. These findings hold important and surprising implications for work in political science on a number of levels. This paper seeks to outline some of these findings, and explore how they might inform political science in unexpected ways. This paper attempts to posit an alternative view of decision making which rests on emotional, as opposed to cognitive, notions of rationality. This paper will begin with a brief discussion of some older models of emotion and emotional processing. This will be followed by a brief overview of recent neuroscientific

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