Abstract:

Possibilistic Defeasible Logic Programming (\Pdelp) is a logic programming language which combines features from argumentation theory and logic programming, incorporating as well the treatment of possibilistic uncertainty and fuzzy knowledge at object-language level. Solving a \Pdelp\ query $Q$ accounts for performing an exhaustive analysis of arguments and defeaters for $Q$, resulting in a so-called dialectical tree, usually computed in a depth-first fashion. Computing dialectical trees efficiently in \Pdelp\ is an important issue, as some dialectical trees may be computationally more expensive than others which lead to equivalent results. In this paper we explore different aspects concerning how to speed up dialectical inference in \Pdelp. We introduce definitions which allow to characterize dialectical trees constructively rather than declaratively, identifying relevant features for pruning the associated search space. The resulting approach can be easily generalized to be applied in other argumentation frameworks based in logic programming.

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