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首页> 外文期刊>Theory and Practice of Logic Programming >Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming
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Reasoning and planning with sensing actions, incomplete information, and static causal laws using answer set programming

机译：使用答案集编程进行感知动作，不完整信息和静态因果律的推理和计划

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摘要

We extend the O-approximation of sensing actions and incomplete information in Son and Baral (2001) to action theories with static causal laws and prove its soundness with respect to the possible world semantics. We also show that the conditional planning problem with-respect to this approximation is NP-complete. We then present an answer set programming based conditional planner, called ascp, that is capable of generating both conformant plans and conditional plans in the presence of sensing actions, incomplete information about the initial state, and static causal laws. We prove the correctness of our implementation and argue that our planner is sound and complete with respect to the proposed approximation. Finally, we present experimental results comparing ascp to other planners.
机译：我们将Son和Baral（2001）中的感知动作和不完整信息的O近似扩展到具有静态因果律的动作理论，并就可能的世界语义证明了其合理性。我们还表明，关于这种近似的条件计划问题是NP完全的。然后，我们提出一个基于答案集编程的条件计划程序，称为ascp，它能够在存在感应动作，有关初始状态的不完整信息以及静态因果律的情况下，生成一致的计划和条件计划。我们证明了实施的正确性，并认为我们的规划师对于拟议的近似方法而言是健全而完整的。最后，我们提出了将ascp与其他计划者进行比较的实验结果。

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