We extend the 0-approximation of sensing actions and incomplete informationin [Son and Baral 2000] to action theories with static causal laws and proveits soundness with respect to the possible world semantics. We also show thatthe conditional planning problem with respect to this approximation isNP-complete. We then present an answer set programming based conditionalplanner, called ASCP, that is capable of generating both conformant plans andconditional plans in the presence of sensing actions, incomplete informationabout the initial state, and static causal laws. We prove the correctness ofour implementation and argue that our planner is sound and complete withrespect to the proposed approximation. Finally, we present experimental resultscomparing ASCP to other planners.
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机译：我们将[Son and Baral 2000]中的感应动作和不完整信息的0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，它能够在存在感知动作，有关初始状态的不完整信息以及静态因果规律的情况下，生成一致的计划和条件计划。我们证明了我们实施的正确性，并认为我们的计划者对于拟议的近似方法而言是健全而完整的。最后，我们将实验结果与其他计划者进行比较。
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