Abstract
In this paper a planning framework based on Ant Colony Optimization techniques is presented. Optimal planning is a very hard computational problem which has been coped with different methodologies. Approximate methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. Our proposal is to use an Ant Colony Optimization approach, based both on backward and forward search over the state space, using different pheromone models and heuristic functions in order to solve sequential optimization planning problems. © Springer-Verlag 2009.
Cite
CITATION STYLE
Baioletti, M., Milani, A., Poggioni, V., & Rossi, F. (2009). Optimal planning with ACO. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5883 LNAI, pp. 212–221). https://doi.org/10.1007/978-3-642-10291-2_22
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