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首页> 外文会议>International Conference on Automated Planning and Scheduling(ICAPS 2006); 2006; >Safe LTL Assumption-Based Planning
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Safe LTL Assumption-Based Planning

机译：基于LTL安全假设的计划

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摘要

Planning for partially observable, nondeterministic domains is a very significant and computationally hard problem. Often, reasonable assumptions can be drawn over expectedominal dynamics of the domain; using them to constrain the search may lead to dramatically improve the efficiency in plan generation. In turn, the execution of assumption-based plans must be monitored to prevent runtime failures that may happen if assumptions turn out to be untrue, and to replan in that case. In this paper, we use an expressive temporal logic, LTL, to describe assumptions, and we provide two main contributions. First, we describe an effective, symbolic forward-chaining mechanism to build (conditional) assumption-based plans for partially observable, nondeterministic domains. Second, we constrain the algorithm to generate safe plans, i.e. plans guaranteeing that, during their execution, the monitor will be able to univo-cally distinguish whether the domain behavior is one of those planned for or not. This is crucial to inhibit any chance of useless replanning episodes. We experimentally show that exploiting LTL assumptions highly improves the efficiency of plan generation, and that by enforcing safety we improve plan execution, inhibiting useless and expensive replanning episodes, without significantly affecting plan generation.
机译：规划部分可观察的不确定性域是一个非常重要且计算困难的问题。通常，可以根据域的预期/名义动态得出合理的假设。使用它们来约束搜索可能会大大提高计划生成的效率。反过来，必须监视基于假设的计划的执行，以防止在假设不正确的情况下可能发生的运行时故障，并在这种情况下进行重新计划。在本文中，我们使用表达性时态逻辑LTL来描述假设，并提供了两个主要贡献。首先，我们描述了一种有效的，象征性的正向链接机制，可为部分可观察的非确定性领域构建（有条件的）基于假设的计划。其次，我们限制算法以生成安全计划，即计划保证在其执行期间，监视器将能够唯一地区分域行为是否是计划的行为之一。这对于抑制任何无用的重新计划情节至关重要。我们通过实验表明，利用LTL假设可以极大地提高计划生成的效率，并且通过加强安全性，我们可以改善计划执行，抑制无用且昂贵的重新计划事件，而不会显着影响计划生成。

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