AAAI Publications, Nineteenth International Conference on Automated Planning and Scheduling

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Ant Search Strategies For Planning Optimization
Marco Baioletti, Alfredo Milani, Valentina Poggioni, Fabio Rossi

Last modified: 2009-10-16

Abstract


In this paper a planning framework based on Ant Colony Optimization techniques is presented. It is well known that finding optimal solutions to planning problems is a very hard computational problem. Stochastic methods do not guarantee either optimality or completeness, but it has been proved that in many applications they are able to find very good, often optimal, solutions. We propose several approaches based both on backward and forward search over the state space, using several heuristics and testing different pheromone models in order to solve sequential optimization planning problems.

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