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在不完整的域中规划和行动
Planning and Acting in Incomplete Domains |
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| 课程网址: |
http://videolectures.net/icaps2011_bryce_domains/
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| 主讲教师: |
Daniel Bryce
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| 开课单位: |
犹他州立大学
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| 开课时间: |
2011-07-21
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| 课程语种: |
英语
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| 中文简介: |
由于人为错误或缺乏领域知识，工程完整的规划域描述通常非常昂贵。学习完整的域名描述也非常具有挑战性，因为许多功能与实现目标无关，数据可能很少。我们提出了一个计划者和代理人，分别通过i）合成计划以避免因无知域模型而导致执行失败，以及ii）在执行期间被动地学习域模型以改进以后的重新计划尝试，从而分别计划和行动在不完整的域中。我们的计划员DeFault是第一个推断域名不完整以避免潜在计划失败的人。 DeFault计算计划中每个操作和状态的失败解释，并计算将发生失败的不完整域的解释数。我们通过计算主要蕴涵（故障诊断）而不是命题模型来证明DeFault表现最佳。我们的代理人守门员在监控其状态时了解不完整指定操作的前提条件和影响，并结合DeFault计划失败解释，可以诊断过去和未来的操作失败。我们通过推理不完整性（而不是忽略它）来证明守门员失败并重新计划更少并且执行更少的行动。
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| 课程简介: |
Engineering complete planning domain descriptions is often very costly because of human error or lack of domain knowledge. Learning complete domain descriptions is also very challenging because many features are irrelevant to achieving the goals and data may be scarce. We present a planner and agent that respectively plan and act in incomplete domains by i) synthesizing plans to avoid execution failure due to ignorance of the domain model, and ii) passively learning about the domain model during execution to improve later re-planning attempts. Our planner DeFault is the first to reason about a domain’s incompleteness to avoid potential plan failure. DeFault computes failure explanations for each action and state in the plan and counts the number of interpretations of the incomplete domain where failure will occur. We show that DeFault performs best by counting prime implicants (failure diagnoses) rather than propositional models. Our agent Goalie learns about the preconditions and effects of incompletely-specified actions while monitoring its state and, in conjunction with DeFault plan failure explanations, can diagnose past and future action failures. We show that by reasoning about incompleteness (as opposed to ignoring it) Goalie fails and re-plans less and executes fewer actions.
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| 关 键 词: |
无知域模型; 学习域模型; 不完整域
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-04-16：lxf
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| 阅读次数: |
40
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