A very well known reasoning model in AI is the BDI (Belief-Desire-Intention). A BDI agent should be able to choose the more rational action to be done with bounded resources and incomplete knowledge in an acceptable time. Although humans need emotions in order to make immediate decisions with incomplete information, traditional BDI models do not take into account affective states of the agent. In this paper we present an implementation of the appraisal process of emotions in BDI agents using a BDI language that integrates logic and probabilist reasoning. Specifically, we implement the event-generated emotions with consequences for self based on the OCC cognitive psychological theory of emotions. We also present an illustrative scenario and its implementation. One original aspect of this work is that we implement the emotions intensity using a probabilistic extension of a BDI language. This intensity is defined by the desirability central value, as pointed by the OCC model. In this way, our implementation of emotional BDI allows to differentiate between emotions and affective reactions. This is an important aspect because emotions tend to generate stronger response. Besides, the intensity of the emotion also determines the intensity of an individual reaction.
['João Carlos Gluz', 'Patrícia Augustin Jaques']
A Probabilistic Implementation of Emotional BDI Agents