Ant search strategies for planning optimization

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. Copyright © 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.