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A Hybrid Linear Programming and Relaxed Plan Heuristic for Partial Satisfaction Planning Problems

机译:部分满意计划问题的混合线性规划和松弛计划启发式

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The availability of informed (but inadmissible) planning heuristics has enabled the development of highly scalable planning systems. Due to this success, a body of work has grown around modifying these heuristics to handle extensions to classical planning. Most recently, there has been an interest in addressing partial satisfaction planning problems, but existing heuristics fail to address the complex interactions that occur in these problems between action and goal selection. In this paper we provide a unique admissible heuristic based on linear programming that we use to solve a relaxed version of the partial satisfaction planning problem. We incorporate this heuristic in conjunction with a lookahead strategy in a branch and bound algorithm to solve a class of over-subscribed planning problems.
机译:知情的(但不允许的)计划启发式方法的可用性使高度可扩展的计划系统得以开发。由于取得了这一成功,围绕修改这些启发式方法以处理对经典计划的扩展已经有了很多工作。最近,人们有兴趣解决部分满意度计划问题,但是现有的启发式方法无法解决行动和目标选择之间在这些问题中发生的复杂相互作用。在本文中,我们基于线性规划提供了一种独特的可允许启发式方法,用于解决部分满意计划问题的宽松版本。我们将这种启发式方法与超前策略结合在分支定界算法中,以解决一类超额预定的规划问题。

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