Introducing Relevance Awareness in BDI Agents

摘要

Artificial agents engaged in real world applications require accurate allocation strategies in order to better balance the use of their bounded resources. In particular, during their epistemic activities, they should be able to filter out all irrelevant information and just consider what is relevant for the current task that they are trying to solve. The aim of this work is to propose a mechanism of relevance-based belief update to be implemented in a BDI cognitive agent. This is in order to improve the performance of agents in information-rich environments. In the first part of the paper we present the formal and abstract model of the mechanism. In the second part we present its implementation in the Jason programming platform and we discuss its performance in simulation trials.