http://popups.lib.uliege.be/1373-5411/index.php?id=1770 Index terms fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=2433 Most of the existing models of intelligent software agents fail to consider an important aspect of human behavior, namely the impact of emotions on processes such as motivation, decision-making, planning, learning, and anticipation. The paper presents an emotional reasoning model of artificial agents, called Belief-Desire-Emotion( BDE). The model is built upon the influential Belief-Desire-Intention agent architecture and follows the Ortony-Clore-Collin's cognitive appraisal theory of human beings. We describe the different stages of the emotion generation process and emphasize how this process influence theoretical reasoning, such as belief and desire revision, and practical reasoning, such as means-end analysis. Additionally, we propose a set of basic emotions, and we exemplify how they are generated and the way they influence the behavior of our BDE agents. Tue, 20 Aug 2024 12:19:23 +0200 Tue, 08 Oct 2024 17:21:14 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2433 http://popups.lib.uliege.be/1373-5411/index.php?id=1767 Anticipatory processes take into account of the contextual events occurring in the environment to anticipate probable upcoming events, and to select the best behavioral responses. The necessary knowledge for prediction of events adapted to context can be learned by classical associative conditioning, which allows associations between events occurring close in a sequence. Context can then correspond to events perceived in the environment as well as to the reinforcing valence of the event eliciting emotional states in the system, both orienting anticipations in memory. Knowledge for anticipation of adapted behaviors to context can be learned by operant reinforced conditioning, which allows associations between behaviors and reinforcing events in the environment, as a function of the reinforcing valence of the event (positive or negative). In this case the processing of a contextual event can select behavioral responses orienting the system to positive reinforcers rather than to negative reinforcers. An attractor neural network model is proposed to account for the different types of anticipatory processes presented as well as for the leaming principles of conditioning allowing adapted anticipations. Tue, 16 Jul 2024 15:30:43 +0200 Tue, 16 Jul 2024 15:30:58 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1767