Metaphysics of Planning Domain Descriptions
Metaphysics of Planning Domain Descriptions
复制标题
规划领域描述的形而上学
DOI：
10.1609/aaai.v30i1.10118
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
A. Pinto
中科院分区：
文献类型：
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作者：
Siddharth Srivastava;Stuart J. Russell;A. Pinto
类STRIP语言（SLL）促进了自动规划的巨大进步。在实践中，SLL用于表达现实世界规划问题的高度抽象版本，从而产生更简洁的模型和更快的解决方案。不幸的是，正如我们在论文中所展示的那样，抽象可解决的现实世界问题的简单方法可能会导致SLL模型无法解决，SLL模型的解决方案与现实世界的问题不正确，或者模型在SLL中无法表达。有一些证据表明，这些限制限制了人工智能规划技术在真实的世界中的适用性，这在机器人技术中的任务和运动规划中很明显。我们表明，这种情况可以得到改善的组合增加的表达能力-例如，允许天使的非决定性的行动效果-和新的算法方法，旨在产生正确的解决方案，从最初不正确的或非马尔可夫抽象模型。
STRIPS-like languages (SLLs) have fostered immense advances in automated planning. In practice, SLLs are used to express highly abstract versions of real-world planning problems, leading to more concise models and faster solution times. Unfortunately, as we show in the paper, simple ways of abstracting solvable real-world problems may lead to SLL models that are unsolvable, SLL models whose solutions are incorrect with respect to the real-world problem, or models that are inexpressible in SLLs. There is some evidence that such limitations have restricted the applicability of AI planning technology in the real world, as is apparent in the case of task and motion planning in robotics. We show that the situation can be ameliorated by a combination of increased expressive power — for example, allowing angelic nondeterminism in action effects — and new kinds of algorithmic approaches designed to produce correct solutions from initially incorrect or non-Markovian abstract models.
DOI：
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发表时间：
2012
期刊：
--
影响因子：
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作者：
Gregory, P
通讯作者：
Gregory, P