Abstract
In this paper we describe a first attempt to solve planning problems through an Ant Colony Optimization approach. We have implemented an ACO algorithm, called ACOPlan, which is able to optimize the solutions of propositional planning problems, with respect to the plans length. Since planning is a hard computational problem, metaheuristics are suitable to find good solutions in a reasonable computation time. Preliminary experiments are very encouraging, because ACOPlan sometimes finds better solutions than state of art planning systems. Moreover, this algorithm seems to be easily extensible to other planning models.
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Baioletti, M., Milani, A., Poggioni, V., Rossi, F. (2009). An ACO Approach to Planning. In: Cotta, C., Cowling, P. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2009. Lecture Notes in Computer Science, vol 5482. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01009-5_7
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DOI: https://doi.org/10.1007/978-3-642-01009-5_7
Publisher Name: Springer, Berlin, Heidelberg
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