Knowledge Representation, Reasoning, and Declarative Problem Solving
Knowledge Representation, Reasoning, and Declarative Problem Solving
Representing Knowledge in A-Prolog
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Building knowledge systems in a-prolog
Building knowledge systems in a-prolog
Answer set based design of knowledge systems
Annals of Mathematics and Artificial Intelligence
Probabilistic reasoning with answer sets
Theory and Practice of Logic Programming
Justifications for logic programs under answer set semantics
Theory and Practice of Logic Programming
Integrating answer set programming and constraint logic programming
Annals of Mathematics and Artificial Intelligence
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
CR-Prolog as a Specification Language for Constraint Satisfaction Problems
LPNMR '09 Proceedings of the 10th International Conference on Logic Programming and Nonmonotonic Reasoning
Negotiation using logic programming with consistency restoring rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Enhancing ASP systems for planning with temporal constraints
LPNMR'07 Proceedings of the 9th international conference on Logic programming and nonmonotonic reasoning
Integrating answer set reasoning with constraint solving techniques
FLOPS'08 Proceedings of the 9th international conference on Functional and logic programming
Reasoning about the intentions of agents
Logic Programs, Norms and Action
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CR-Prolog is an extension of the knowledge representation language A-Prolog. The extension is built around the introduction of consistency-restoring rules (cr-rules for short), and allows an elegant formalization of events or exceptions that are unlikely, unusual, or undesired. The flexibility of the language has been extensively demonstrated in the literature, with examples that include planning and diagnostic reasoning. In this paper we present the design of an inference engine for CR-Prolog that is efficient enough to allow the practical use of the language for medium-size applications. The capabilities of the inference engine have been successfully demonstrated with experiments on an application independently developed for use by NASA.