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In this paper we first define a recursive semantics for warranted formulas in a general defeasible argumentation framework by formalizing a notion of collective (non-binary) conflict among arguments. The recursive semantics for warranted formulas is based on the fact that if the argument is rejected, then all arguments built on it should also be rejected. The main characteristic of our recursive semantics is that an output (extension) of a knowledge base is a pair of sets of warranted and blocked formulas. Arguments for both warranted and blocked formulas are recursively based on warranted formulas but, while warranted formulas do not generate any collective conflict, blocked conclusions do. Formulas that are neither warranted nor blocked correspond to rejected formulas. Second we extend the general defeasible argumentation framework by attaching levels of preference to defeasible knowledge items and by providing a level-wise definition of warranted and blocked formulas. Third we formalize the warrant recursive semantics for the particular framework of Possibilistic Defeasible Logic Programming, we call this particular framework Recursive Possibilistic Defeasible Logic Programming (\mbox{RP-DeLP} for short), and we show its relevance in the scope of Political debates. An RP-DeLP program may have multiple outputs in case of circular definitions of conflicts among arguments. So, we tackle the problem of which output one should consider for an RP-DeLP program with multiple outputs. To this end we define the maximal ideal output of an RP-DeLP program as the set of conclusions which are ultimately warranted and we present an algorithm for computing them in polynomial space and with an upper bound on complexity equal to P^{NP}. Finally, we propose an efficient and scalable implementation of this algorithm that is based on implementing the two main queries of the system, looking for valid arguments and collective conflicts between arguments, using SAT encodings. We perform an experimental evaluation of our SAT based approach when solving test sets of instances with single and multiple preference levels for defeasible knowledge.
The authors are very thankful to the anonymous reviewers for their helpful and constructive
comments. This research was partially supported by the Spanish projects ARINF (TIN2009-
14704-C03-01), TASSAT (TIN2010-20967-C04-03), EdeTRI (TIN2012-39348-C02-01) and AT
(CONSOLIDER- INGENIO 2010, CSD2007-00022). |
