Answer set programming and plan generation
Artificial Intelligence
Extending and implementing the stable model semantics
Artificial Intelligence
Logic programs with stable model semantics as a constraint programming paradigm
Annals of Mathematics and Artificial Intelligence
ASSAT: computing answer sets of a logic program by SAT solvers
Artificial Intelligence - Special issue on nonmonotonic reasoning
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
Answer Set Programming Based on Propositional Satisfiability
Journal of Automated Reasoning
BerkMin: A fast and robust Sat-solver
Discrete Applied Mathematics
Combining answer set programming with description logics for the Semantic Web
Artificial Intelligence
Exploiting conjunctive queries in description logic programs
Annals of Mathematics and Artificial Intelligence
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Handbook of Satisfiability: Volume 185 Frontiers in Artificial Intelligence and Applications
Realizing Default Logic over Description Logic Knowledge Bases
ECSQARU '09 Proceedings of the 10th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
ICLP '09 Proceedings of the 25th International Conference on Logic Programming
Equilibria in heterogeneous nonmonotonic multi-context systems
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
A uniform integration of higher-order reasoning and external evaluations in answer-set programming
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Semantics and complexity of recursive aggregates in answer set programming
Artificial Intelligence
Potassco: The Potsdam Answer Set Solving Collection
AI Communications - Answer Set Programming
Pushing efficient evaluation of HEX programs by modular decomposition
LPNMR'11 Proceedings of the 11th international conference on Logic programming and nonmonotonic reasoning
Answer set programming at a glance
Communications of the ACM
A comparison of reasoning techniques for querying large description logic ABoxes
LPAR'06 Proceedings of the 13th international conference on Logic for Programming, Artificial Intelligence, and Reasoning
Effective integration of declarative rules with external evaluations for semantic-web reasoning
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
Conflict-driven answer set solving: From theory to practice
Artificial Intelligence
Asp modulo csp: The clingcon system
Theory and Practice of Logic Programming
Exploiting unfounded sets for HEX-Program evaluation
JELIA'12 Proceedings of the 13th European conference on Logics in Artificial Intelligence
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Answer Set Programming (ASP) is a well-known problem solving approach based on nonmonotonic logic programs and efficient solvers. To enable access to external information, hex-programs extend programs with external atoms, which allow for a bidirectional communication between the logic program and external sources of computation (e.g., description logic reasoners and Web resources). Current solvers evaluate hex-programs by a translation to ASP itself, in which values of external atoms are guessed and verified after the ordinary answer set computation. This elegant approach does not scale with the number of external accesses in general, in particular in presence of nondeterminism (which is instrumental for ASP). In this paper, we present a novel, native algorithm for evaluating hex-programs which uses learning techniques. In particular, we extend conflict-driven ASP solving techniques, which prevent the solver from running into the same conflict again, from ordinary to hex-programs. We show how to gain additional knowledge from external source evaluations and how to use it in a conflict-driven algorithm. We first target the uninformed case, i.e., when we have no extra information on external sources, and then extend our approach to the case where additional meta-information is available. Experiments show that learning from external sources can significantly decrease both the runtime and the number of considered candidate compatible sets.