Data di Pubblicazione:
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.
Tipologia CRIS:
4.1 Contributo in Atti di convegno
Keywords:
Planning; Ant Colony Optimization; Heuristic functions
Elenco autori:
Baioletti, Marco; Milani, Alfredo; Poggioni, Valentina; Rossi, Fabio
Link alla scheda completa:
Titolo del libro:
Proc of the 9th European Conference on Evolutionary Computation in Combinatorial Optimisation