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A Heuristic Search Approach To Planning With Temporally Extended Preferences

机译：具有临时扩展首选项的启发式搜索方法

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摘要

Planning with preferences involves not only finding a plan that achieves the goal, it requires finding a preferred plan that achieves the goal, where preferences over plans are specified as part of the planner's input. In this paper we provide a technique for accomplishing this objective. Our technique can deal with a rich class of preferences, including so-called temporally extended preferences (TEPs). Unlike simple preferences which express desired properties of the final state achieved by a plan, TEPs can express desired properties of the entire sequence of states traversed by a plan, allowing the user to express a much richer set of preferences. Our technique involves converting a planning problem with TEPs into an equivalent planning problem containing only simple preferences. This conversion is accomplished by augmenting the inputed planning domain with a new set of predicates and actions for updating these predicates. We then provide a collection of new heuristics and a specialized search algorithm that can guide the planner towards preferred plans. Under some fairly general conditions our method is able to find a most preferred plan-i.e., an optimal plan. It can accomplish this without having to resort to admissible heuristics, which often perform poorly in practice. Nor does our technique require an assumption of restricted plan length or make-span. We have implemented our approach in the HPlan-P planning system and used it to compete in the 5th International Planning Competition, where it achieved distinguished performance in the Qualitative Preferences track.
机译：具有偏好的计划不仅涉及找到实现目标的计划，还需要找到实现目标的首选计划，其中将对计划的偏好指定为计划者输入的一部分。在本文中，我们提供了一种用于实现此目标的技术。我们的技术可以处理多种偏好，包括所谓的时间扩展偏好（TEP）。与表达计划所实现的最终状态的期望属性的简单偏好不同，TEP可以表达计划所遍历的整个状态序列的期望属性，从而使用户可以表达更为丰富的偏好。我们的技术涉及将带有TEP的计划问题转换为仅包含简单首选项的等效计划问题。通过使用一组新的谓词和用于更新这些谓词的操作来扩展输入的计划域来完成此转换。然后，我们提供了一系列新的启发式方法和专门的搜索算法，可以指导计划者制定首选计划。在一些相当普遍的条件下，我们的方法能够找到最优选的计划，即最佳计划。它可以完成此操作而不必诉诸于在实践中通常表现不佳的可允许的启发式方法。我们的技术也不需要假设计划长度或制造期限受到限制。我们已经在HPlan-P计划系统中实施了我们的方法，并用它来参加了第五届国际计划竞赛，该竞赛在Qualitative Preferences轨道上表现出色。

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