Abstract. The traditional consideration that intelligent behaviors can only be produced from pure reasoning fails when trying to explain most of human behaviors, in which the emotional component has a decisive weight. However, although many different efforts have been made to consider emotions in the rational process, emotion is still perceived by many research areas as a non-desirable quality for a computational system. This is not the case of the field of believable agents, where emotions are well respected, although they are sometimes associated to a certain loss of control. This paper presents the mechanisms proposed by a generic cognitive architecture for agents with emotionally influenced behaviors, called cognitiva, to maintain behaviors control without giving up the richness provided by emotions. This architecture, together with a progressive specification process for its application, have been used successfully to model 3D intelligent virtual agents.

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Abstract — This paper describes a novel algorithm called CON-MODP for computing Pareto optimal policies for deterministic multi-objective sequential decision problems. CON-MODP is a value iteration based multi-objective dynamic programming algorithm that only computes stationary policies. We observe that for guaranteeing convergence to the unique Pareto optimal set of deterministic stationary policies, the algorithm needs to perform a policy evaluation step on particular policies that are inconsistent in a single state that is being expanded. We prove that the algorithm converges to the Pareto optimal set of value functions and policies for deterministic infinite horizon discounted multiobjective Markov decision processes. Experiments show that CON-MODP is much faster than previous multi-objective value iteration algorithms. I.

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An advanced Business Game is presented in the paper, built on the methodology of System Dynamics. It can be used for cognitive learning and knowledge transmission in schools and Universities; it allows the learners to take decisions at each time step, after which it calculates the corresponding results, showing them according to the principles of double entry accounting. An agent based framework is then discussed, which constitutes a form of virtual tutorship for the learners. The agents act as a decision support system for the decisions to be taken, and can explain some cause/effect relations. The agents themselves learn how the model work by practicing it, through some reinforcement learning techniques.

This paper concerns emotion modeling for a taskindependent agent by integrating a module of needs. Inspired by theoretical views with regards to human needs, we suggest that appraisals can be confined within various scopes of needs, and to demonstrate this, we propose an emotion framework which allows control over appraisals via pre-defined levels of needs, urgency or priorities. 1.