Insertion of Probabilistic Knowledge into BDI Agents Construction Modeled in Bayesian Networks

Abstract

Achieving faithful representation of knowledge is a historic and still unreached goal in the area of Artificial Intelligence. Computational systems that store knowledge in many different ways have been built in order to emulate the capacity of human knowledge representation, taking into consideration the several inherent difficulties to it. Within this context, this paper proposes an experiment that utilizes two distinct ways of representing knowledge: symbolic, BDI in this case, and probabilistic, Bayesian Networks in this case. In order to develop a proof of concept of this proposal for knowledge representation, examples that will be built through agent oriented programming technology will be used. For that, implementation of a MultiAgent system was developed, extending the \textit{Jason} framework through the implementation of a plug in called \textit{COPA}. For the representation of probabilistic knowledge, a Bayesian Network building tool, also adapted to this system, was used. The case studies showed improvement in the management of uncertain knowledge in relation to the building approaches of classic BDI agents, i.e., that do not use probabilistic knowledge.