Answer Set Programming (ASP) is a constraint-based programming paradigm proposed recently by AI researchers. In this paradigm one solves a problem by encoding constraints as logic program rules in such a way that the solutions to the original problem correspond to the so-called answer sets of the logic program....[ Read more ]

Answer Set Programming (ASP) is a constraint-based programming paradigm proposed recently by AI researchers. In this paradigm one solves a problem by encoding constraints as logic program rules in such a way that the solutions to the original problem correspond to the so-called answer sets of the logic program.

This new programming paradigm has found applications in problems ranging from combinatorial search problems, such as the graph coloring and Hamiltonian circuit problems, to product configuration and data encryption problems. In this dissertation, we make the following two contributions to the new programming paradigm ASP. First, we propose and implement a new ASP system using traditional SAT (propositional satisfiability) solvers. This is done by a translation from logic programs to sets of clauses in propositional logic. Our experimental results show that for many benchmark domains, our system performs better than existing specialized ASP systems. Secondly, we study the phase transition phenomenon in randomly generated logic programs, and identify a region in a class of randomly generated logic programs where the logic programs are hard for all existing ASP systems.