Abstract
It is generally recognized that the use of emotions plays an important role in human interactions, for it leads to more flexible decision-making. In the present work, we extend the idea presented in a paper by Castelfranchi, Conte, and Paolucci, by employing a systematic and detailed model of emotion generation. A scenario is described in which agents that have various types of emotions make decisions regarding compliance with a norm. We compare our results with the ones achieved in previous simulations and we show that the use of emotions leads to a selective behavior which increases agent performance, considering that different types of emotions cause agents to have different acting priorities.
Author partially supported by CNPq.
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Bazzan, A.L.C., Adamatti, D.F., Bordini, R.H. (2002). Extending the Computational Study of Social Norms with a Systematic Model of Emotions. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_11
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DOI: https://doi.org/10.1007/3-540-36127-8_11
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