Prolog and ASP Inference Under One Roof

Answer set programming (ASP) is a declarative programming paradigm stemming from logic programming that has been successfully applied in various domains. Despite amazing advancements in ASP solving, many applications still pose a challenge that is commonly referred to as grounding bottleneck. Devising, implementing, and evaluating a method that alleviates this problem for certain application domains is the focus of this paper. The proposed method is based on combining backtracking-based search algorithms employed in answer set solvers with SLDNF resolution from PROLOG. Using PROLOG inference on non-ground portions of a given program, both grounding time and the size of the ground program can be substantially reduced

Propagators and Solvers for the Algebra of Modular Systems

To appear in the proceedings of LPAR 21. Solving complex problems can involve non-trivial combinations of distinct knowledge bases and problem solvers. The Algebra of Modular Systems is a knowledge representation framework that provides a method for formally specifying such systems in purely semantic terms. Formally, an expression of the algebra defines a class of structures. Many expressive formalism used in practice solve the model expansion task, where a structure is given on the input and an expansion of this structure in the defined class of structures is searched (this practice overcomes the common undecidability problem for expressive logics). In this paper, we construct a solver for the model expansion task for a complex modular systems from an expression in the algebra and black-box propagators or solvers for the primitive modules. To this end, we define a general notion of propagators equipped with an explanation mechanism, an extension of the alge- bra to propagators, and a lazy conflict-driven learning algorithm. The result is a framework for seamlessly combining solving technology from different domains to produce a solver for a combined system.Comment: To appear in the proceedings of LPAR 2

Relating Constraint Answer Set Programming Languages and Algorithms

Recently a logic programming language AC was proposed by Mellarkod et al. (2008) to integrate answer set programming and constraint logic programming. Soon after that, a CLINGCON language integrating answer set programming and finite domain constraints, as well as an EZCSP language integrating answer set programming and constraint logic programming were introduced. The development of these languages and systems constitutes the appearance of a new AI subarea called constraint answer set programming. All these languages have something in common. In particular, they aim at developing new efficient inference algorithms that combine traditional answer set programming procedures and other methods in constraint programming. Yet, the exact relation between the constraint answer set programming languages and the underlying systems is not well understood. In this paper we address this issue by formally stating the precise relation between several constraint answer set programming languages - AC, CLINGCON, EZCSP - as well as the underlying systems