Plausibility Reasoning via Projected Answer Set Counting - A Hybrid Approach

Abstract

Answer set programming is a form of declarative programming widely used to solve difficult search problems. Probabilistic applications however require to go beyond simple search for one solution and need counting. One such application is plausibility reasoning, which provides more fine-grained reasoning mode between simple brave and cautious reasoning. When modeling with ASP, we oftentimes introduce auxiliary atoms in the program. If these atoms are functionally independent of the atoms of interest, we need to hide the auxiliary atoms and project the count to the atoms of interest resulting in the problem projected answer set counting. In practice, counting becomes quickly infeasible with standard systems such as clasp. In this paper, we present a novel hybrid approach for plausibility reasoning under projections, thereby relying on projected answer set counting as basis. Our approach combines existing systems with fast dynamic programming, which in our experiments shows advantages over existing ASP systems.