A possibilistic defeasible logic programming approach to argumentation-based decision-making (2014)

AbstractThe development of symbolic approaches to decision-making has become an ever-growing 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 decision-maker, 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 relevant information regarding the agent's preferences is specified, its choice behaviour coincides with respect to the optimum preference derived from a rational preference relation.Keywords:: non-monotonic reasoningargumentationpossibilistic defeasible logic programmingdecision making AcknowledgementsThis work is partially supported by Universidad Nacional de San Luis (PROICO 30312) and Secretaría General de Ciencia y Tecnología – Universidad Nacional del Sur. We also want to thank Mauro Gómez Lucero for useful discussions on the topic of this article.Notes1. In Order Theory, a binary relation satisfying these properties is called a total pre-order.2. The necessity degree of refers to the degree of possibilistic entailment of q.3. This example has been taken from (Gómez et al., Citation2009), please refer to this source to see how the necessity degree values were calculated using One-Complement accrual function.4. Remember that is parameterised with respect to a user-specified ACC function to aggregate necessity degrees (see Definition 3.3).5. The proof of this proposition as well as the proofs of the remaining propositions, lemmas and theorems in this article, can be found in Appendix A.6. Two alternatives have the same properties if their attribute values coincide for all the preference criteria used to compare them.7. These values have been arbitrarily assigned to reflex the priority among the attribute values of the neighbourhood noise preference criterion.8. To simplify notation, given an argument , only the set of uncertain clauses will be given since the conclusion h and its associated necessity degree can be obtained from it.9. This function is a variant of the One-Complement accrual function used in (Gómez et al. Citation2009) where K aims at weighting the importance given to the highest priority preference criterion.