Preference Logic Programming

Preference logic programming (PLP) is an extension of constraint logic programming (CLP) for declaratively specifying problems requiring optimization or comparison and selection among alternative solutions to a query. In the PLP framework, the deenite clauses of a constraint logic program are augmented by two new kinds of clauses, which we call optimization clauses and arbiter clauses. Optimization clauses specify which predicates are to be optimized and arbiter clauses specify the criteria to be used for optimization. We illustrate their use with representative examples: one from dynamic programming and another from ambiguity resolution in grammars. We formalize the semantics of PLP using concepts from modal logic: Essentially, each world in the possible-worlds semantics for a preference logic program is a model of the program, and an ordering over these worlds is enforced by the arbiter clauses in the program. We introduce a new notion called preferential consequence to refer to truth in the optimal worlds. Because we are interested in computing preferential consequences (as opposed to the logical consequences), we are able to provide a more eecient inference procedure than that for modal logic. We describe a sound operational semantics that is an extension of SLD derivation that prunes the search paths of suboptimal solutions. This operational semantics is complete for the class of stratiied preference logic programs.