DATALOG with Constraints — An Answer-Set Programming System

Book One

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 17

Constraint Satisfaction

Answer-set programming (ASP) has emerged recently as a viable programming paradigm well attuned to search problems in AI, constraint satisfaction and combinatorics. Propositional logic is, arguably, the simplest ASP system with an intuitive semantics supporting direct modeling of problem constraints. However, for some applications, especially those requiring that transitive closure be computed, it requires additional variables and results in large theories. Consequently, it is not a pratical computational tool for such problems. On the other hand, ASP systems based on nonmonotonic logics, such as stable logic programming, can handle transitive closure computation efficiently and, in general, often offer very concise theories as problem representations. Their semantics are, however, more complex. Searching for the middle ground, in this paper we introduce a new nonmonotonic logic, DATALOG with constraints or DC. Informally, DC theories consist of propositional clauses (constraints) and of Horn rules (DATALOG). The semantics of DC is a simple and natural extension of the semantics of the propositional logic. However, thanks to the presence of Horn rules in the system, modeling of transitive closure becomes straightforward. We describe the syntax and semantics of DC, and study its properties. We discuss an implementation of DC and present results of experimental study of the effectiveness of DC, comparing it with the CSAT satisfiability checker and SMODELS implementation of stable logic programming. Our results show that DC is competitive with the other two approaches, in the case of many search problems, yielding much more efficient solutions.