AAAI Publications, Twenty-Second International FLAIRS Conference

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ACOPlan: Planning with Ants
Marco Baioletti, Alfredo Milani, Valentina Poggioni, Fabio Rossi

Last modified: 2009-03-17


In this paper an application of the metaheuristic Ant Colony Optimization to optimal planning is presented. It is well known that finding out optimal solutions to planning problem is a very hard computational problem. 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 solutions, often close to optimal ones. Since one of the most performing stochastic method for combinatorial optimization is ACO, we have decided to use this technique to design an algorithm which optimizes plan length in propositional planning. This algorithm has been implemented and some empirical evaluations have been performed. The results obtained are encouraging and show the feasibility of this approach.


Planning; Ant Colony Optimization; Heuristic Search

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