A Possibilistic Defeasible Logic Programming Approach to Argumentation-Based Decision Making

Abstract

The development of symbolic approaches to decision-making has become an evergrowing research line in artificial intelligence; argumentation has contributed to that with its unique strengths. Following this trend, this article proposes a general-purpose decision framework based on argumentation. Given a set of alternatives posed to the decisionmaker, the framework represents the agent’s preferences and knowledge by an epistemic component developed using possibilistic defeasible logic programming. The reasons by which a particular alternative is deemed better than another are explicitly considered in the argumentation process involved in warranting information from the epistemic component. The information warranted by the dialectical process is then used in decision rules that implement the agent’s general decision-making policy. Essentially, decision rules establish patterns of behaviour of the agent specifying under which conditions a set of alternatives will be considered acceptable; moreover, a methodology for programming the agent’s epistemic component is defined. It is demonstrated that programming the agent’s epistemic component following this methodology exhibits some interesting properties with respect to the selected alternatives; also, when all the relevantinformation regarding the agent’s preferences is specified, its choice behaviour coincides with respect to the optimum preference derived from a rational preference relation.