Journal of the ACM (JACM)
Complexity results for disjunctive logic programming and application to nonmonotonic logics
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
Artificial Intelligence
New ideas in optimization
Future Generation Computer Systems
Future Generation Computer Systems
Declarative problem-solving in DLV
Logic-based artificial intelligence
Knowledge Representation with Logic Programs
LPKR '97 Selected papers from the Third International Workshop on Logic Programming and Knowledge Representation
New Generation Systems for Non-monotonic Reasoning
LPNMR '01 Proceedings of the 6th International Conference on Logic Programming and Nonmonotonic Reasoning
Comparing a Pair-Wise Compatibility Heuristic and Relaxed Stratification: Some Preliminary Results
ECSQARU '01 Proceedings of the 6th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty
Local Search Techniques for Disjunctive Logic Programs
AI*IA '99 Proceedings of the 6th Congress of the Italian Association for Artificial Intelligence on Advances in Artificial Intelligence
Graph theoretical characterization and computation of answer sets
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
The DLV system for knowledge representation and reasoning
ACM Transactions on Computational Logic (TOCL)
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Answer Set Programming is a very convenient framework to represent various problems issued from Artificial Intelligence (nonmonotonic reasoning, planning, diagnosis...). Furthermore, it can be used to neatly encode combinatorial problems. In all cases, the solutions are obtained as sets of literals: the Answer Sets.Ant Colony Optimization is a general metaheuristics that has been already successfully used to solve hard combinatorial problems (traveling salesman problem, graph coloring, quadratic assignment...). It is based on the collective behavior of artificial ants exploring a graph and exchanging pieces of information by means of pheromone traces.The purpose of this work is to show how Ant Colony Optimization can be used to compute an answer set of a logic program.