Abstract
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. © 2002 Springer-Verlag.
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CITATION STYLE
Nicolas, P., Saubion, F., & Stéphan, I. (2002). Answer set programming by ant colony optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2424 LNAI, pp. 481–492). Springer Verlag. https://doi.org/10.1007/3-540-45757-7_40
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