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Solving Simple Planning Problems with More Inference and No Search

机译:通过更多的推理无需搜索即可解决简单的计划问题

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摘要

Many benchmark domains in AI planning including Blocks, Logistics, Gripper, Satellite, and others lack the interactions that characterize puzzles and can be solved non-optimally in low polynomial time. They are indeed easy problems for people, although as with many other problems in AI, not always easy for machines. In this paper, we address the question of whether simple problems such as these can be solved in a simple way, i.e., without search, by means of a domain-independent planner. We address this question empirically by extending the constraint-based planner CPT with additional domain-independent inference mechanisms. We show then for the first time that these and several other benchmark domains can be solved with no backtracks while performing only polynomial node operations. This is a remarkable finding in our view that suggests that the classes of problems that are solvable without search may be actually much broader than the classes that have been identified so far by work in Tractable Planning.
机译:人工智能规划中的许多基准域,包括区块,物流,抓取器,卫星等,都缺乏以难题为特征的交互作用,因此可以在较短的多项式时间内非最优地求解。尽管与人工智能中的许多其他问题一样,它们对于人来说确实是容易解决的问题,但对于机器而言却并非总是那么容易。在本文中,我们解决了这样的问题:是否可以通过独立于域的计划程序以简单的方式(例如,无需搜索)解决诸如此类的简单问题。我们通过扩展基于约束的计划程序CPT与其他独立于域的推理机制,以经验方式解决此问题。然后,我们首次展示了仅执行多项式节点运算,就可以无回溯地解决这些以及其他几个基准域。在我们看来,这是一个非凡的发现,表明无需搜索就可以解决的问题类别实际上可能比迄今为止在《可实践规划》中确定的类别要广泛得多。

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