Using arguments for making decisions: A possibilistic logic approach
This work addresses the need for formal models in decision-making for AI systems, offering a novel integration of argumentation and possibility theory, but it is incremental as it builds on existing approaches.
The paper tackles the problem of formalizing argument-based decision-making by proposing a possibilistic logic framework that builds arguments from uncertain knowledge and prioritized goals, computing decisions with pessimistic and optimistic attitudes; it shows agreement with possibility theory when knowledge and goals are consistent and generalizes to handle inconsistencies.
Humans currently use arguments for explaining choices which are already made, or for evaluating potential choices. Each potential choice has usually pros and cons of various strengths. In spite of the usefulness of arguments in a decision making process, there have been few formal proposals handling this idea if we except works by Fox and Parsons and by Bonet and Geffner. In this paper we propose a possibilistic logic framework where arguments are built from an uncertain knowledge base and a set of prioritized goals. The proposed approach can compute two kinds of decisions by distinguishing between pessimistic and optimistic attitudes. When the available, maybe uncertain, knowledge is consistent, as well as the set of prioritized goals (which have to be fulfilled as far as possible), the method for evaluating decisions on the basis of arguments agrees with the possibility theory-based approach to decision-making under uncertainty. Taking advantage of its relation with formal approaches to defeasible argumentation, the proposed framework can be generalized in case of partially inconsistent knowledge, or goal bases.