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.

An ACO approach to planning

BAIOLETTI, Marco;MILANI, Alfredo;POGGIONI, VALENTINA;ROSSI, Fabio
2009

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.
2009
9783642010088
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11391/153974
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