An ACO Approach to Planning

Authors:
Marco Baioletti;Alfredo Milani;Valentina Poggioni;Fabio Rossi
Affiliations:
Dipartimento di Matematica e Informatica, University of Perugia, Italy;Dipartimento di Matematica e Informatica, University of Perugia, Italy;Dipartimento di Matematica e Informatica, University of Perugia, Italy;Dipartimento di Matematica e Informatica, University of Perugia, Italy
Venue:
EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Year:
2009

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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.