An ACO Approach to Planning

机译：ACO规划方法

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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.

机译：在本文中，我们描述了通过蚁群优化方法解决规划问题的首次尝试。我们已经实现了一种称为ACOPlan的ACO算法，该算法能够针对计划长度优化命题计划问题的解决方案。由于计划是一个困难的计算问题，因此元启发法适合在合理的计算时间内找到良好的解决方案。初步实验非常令人鼓舞，因为ACOPlan有时会找到比最先进的计划系统更好的解决方案。而且，该算法似乎很容易扩展到其他计划模型。

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来源
《Evolutionary computation in combinatorial optimization》|2009年|73-84|共12页
会议地点 Tubingen(DE);Tubingen(DE)
作者
Marco Baioletti; Alfredo Milani; Valentina Poggioni; Fabio Rossi;
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作者单位

Dipartimento di Matematica e Informatica University of Perugia, Italy;

Dipartimento di Matematica e Informatica University of Perugia, Italy;

Dipartimento di Matematica e Informatica University of Perugia, Italy;

Dipartimento di Matematica e Informatica University of Perugia, Italy;

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正文语种 eng
中图分类理论、方法;
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An ACO Approach to Planning

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