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An ACO approach to planning

Conference Paper
Publication Date:
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.
Iris type:
4.1 Contributo in Atti di convegno
Keywords:
Planning; Ant Colony Optimization; Heuristic functions
List of contributors:
Baioletti, Marco; Milani, Alfredo; Poggioni, Valentina; Rossi, Fabio
Authors of the University:
MILANI ALFREDO
Handle:
https://iris.unilink.it/handle/20.500.14085/43179
Book title:
Proc of the 9th European Conference on Evolutionary Computation in Combinatorial Optimisation
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URL

https://link.springer.com/chapter/10.1007/978-3-642-01009-5_7
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