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

EDGARDO FERRETTI; MARCELO ERRECALDE; ALEJANDRO JAVIER GARCIA; GUILLERMO R. SIMARI

JOURNAL OF EXPERIMENTAL AND THEORETICAL ARTIFICIAL INTELLIGENCE

TAYLOR & FRANCIS LTD

Lugar: Londres; Año: 2014 vol. 26 p. 519 - 550

0952-813X

The development of symbolic approaches to decision making has become an ever-growing research line in Artificial Intelligence, in particular those based on Argumentation. Following this trend, this paper proposes a general-purpose decision framework based on argumentation. Given a set of alternatives posed to the agent (decision maker), this framework represents the agent´s preferences and knowledge by an epistemic component developed using Possibilistic Defeasible Logic Programming. The reasons by which an 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. Basically, decision rules establish patterns of behaviour of the agent specifying under which conditions a set of alternatives is 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. Besides, when all relevant information about the agent´s preferences is specified, its choice behaviour coincides with respect to the optimum one of a rational preference relation.