Abstract. This paper introduces a neurologically inspired computational model for the emergence of group decisions. The model combines an individual decision making model based on Damasio’s Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of emotions and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group decisions, while, in addition, there is a feeling of wellness with these common decisions amongst the group members. 1

Abstract. This paper introduces a neurologically inspired computational model for the dynamics and diffusion of agent states within groups. The model combines an individual model based on Damasio’s Somatic Marker Hypothesis with mutual effects of group members on each other via mirroring of individual states such as emotions, beliefs and intentions. The obtained model shows how this combination of assumed neural mechanisms can form an adequate basis for the emergence of common group beliefs and intentions, while, in addition there is a positive feeling with these common states amongst the group members. A particular issue addressed is how certain types of states may affect other types of states, for example, emotions have an effect on beliefs and intentions, and beliefs may effect emotions. 1

Although attempts have already been made, computer support of industrial design is still in its infancy, especially of design for aesthetics. The reason is that no methodology is available to incorporate in computer support, design aspects like appearance, pleasantness and human usage of a product. The objective of the paper is to present a methodology that facilitates development of computer support of design for aesthetics. While a designed product can trigger definite aesthetic responses to observers, it is not easy to relate these responses to the characteristics of the product. For that reason the paper focuses on the issues related to a practical coupling of intended aesthetic impressions and shape design. First it summarizes the fundamentals and knowledge related to aesthetics. Then it compares two studies on phenomenological and systematic approaches that have been carried out to model user responses to products and to identify possibilities for designers to influence those res...

Abstract. Emotions can be considered inextricably linked to embodied appraisals- perceptions of bodily states that inform agents of how they are faring in the world relative to their own well-being. Emotionappraisals are thus relational phenomena the relevance of which can be learned or evolutionarily selected for given a reliable coupling between agent-internal and environmental states. An emotion-appraisal attentional disposition permits agents to produce behaviour that exploits such couplings allowing for adaptive agent performance across agentenvironment interactions. This chapter discusses emotions in terms of dynamical processes whereby attentional dispositions are considered central to an understanding of behaviour. The need to reconcile a dynamical systems perspective with an approach that views emotions as attentional dispositions representative of embodied relational phenomena (embodied appraisals) is argued for. Attention and emotion are considered to be features of adaptive agent behaviour that are interdependent in their temporal, structural and organizational relations. Key words: embodied appraisals, dynamical systems, relational phenomena, attentional emotion-appraisal amalgams 1

Abstract. Affective computing has been an extremely active research and development area for some years now, with some of the early results already starting to be integrated in human-computer interaction systems. Driven mainly by research initiatives in Europe, USA and Japan and accelerated by the abundance of processing power and low-cost, unintrusive sensors like cameras and microphones, affective computing functions in an interdisciplinary fashion, sharing concepts from diverse fields, such as signal processing and computer vision, psychology and behavioral sciences, human-computer interaction and design, machine learning, and so on. In order to form relations between low-level input signals and features to high-level concepts such as emotions or moods, one needs to take into account the multitude of psychology and representation theories and research findings related to them and deploy machine learning techniques to actually form computational models of those. This chapter elaborates on the concepts related to affective computing, how these can be connected to measurable features via representation models and how they can be integrated into humancentric applications. 1

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The study of decision making has been dominated by economic perspectives, which model people as rational agents who carefully weigh costs and benefits and try to maximize the utility of every choice, without consideration of issues such as cultural norms, religious beliefs and moral rules. However, psychological findings indicate that in many situations people are not rational decision makers as defined by the economic theories. One of the domains in which traditional cost-benefit models fail to predict human behavior is the domain of moral reasoning. This work presents the first computational model of recognitionbased moral decision making, MoralDM, which integrates several AI techniques in order to model recent psychological findings on moral decision making. MoralDM uses a natural language system to produce formal representations from psychological stimuli,

The execution of an artificial agent is usually implemented with a sense–reason–act cycle. This cycle includes tasks such as event processing, generating and revising plans, and selecting actions to execute. However, there are typically many choices in the design of such a cycle, which are often hard-coded in the cycle in an ad hoc way. The question of this paper is how one decides, in a principled way, how often and which reasoning rules to apply, how to interleave the execution of plans, or when to start replanning. This paper proposes and formalizes the eliciting conditions of hope, fear, joy, and distress according to a well-known psychological model of human emotion. These conditions are then used to reduce the choices an agent can make in each state. They formalize the idea that emotions focus an agent’s attention on what is important in each state.