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On the utility of Plan-space (Causal) Encodings

机译:关于计划空间(因果)编码的实用性

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Recently, casting planning as propositional satisfiability has been shown to be a very promising technique for plan synthesis. Although encodings based both on state-space planning and on plan-space (causal) planning have been proposed, most implementations and trade-off evaluations primarily use state-based encodings. This is surprising given both the prominence of plan-space planners in traditional planning, as well as the recent claim that lifted versions of causal encodings provide the smallest encodings. In this paper we attempt a systematic analytical and empirical comparison of plan-space (causal) encodings and state-space encodings. We start by pointing out the connection between the different ways of proving the correctnhess of a plan, and the spectrum of possible SAT encodings. We then characterize the dimensions along which causal proofs, and consequently, plan-space encodings, can vary. We provide two encodings that are much smaller than those previously proposed. We then show that the smallest causal encodings cannot be smaller in size than the smallest state-based encodings. We shall show that the "lifting" transformation does not affect this relation. Finally, we will present some empirical results that demonstrate that the relative encoding sizes are indeed correlated with the hardness of solving them. We end with a discussion on when the primacy of traditional plan-space planners over state-space planners might carry over to their respective SAT encodings.
机译:最近,已证明将选拔计划作为命题可满足性是计划综合的非常有前途的技术。尽管已经提出了既基于状态空间规划又基于计划空间(因果)规划的编码,但是大多数实现和权衡评估主要使用基于状态的编码。考虑到计划空间计划人员在传统计划中的突出地位,以及最近因因果编码的提升版本提供最小编码的说法,这令人惊讶。在本文中,我们尝试对计划空间(因果)编码和状态空间编码进行系统的分析和经验比较。我们首先指出证明计划正确性的不同方法与可能的SAT编码范围之间的联系。然后,我们描述因果证明以及计划空间编码可能沿其变化的维度。我们提供了两种编码,它们比以前提出的编码要小得多。然后,我们证明最小的因果编码的大小不能小于最小的基于状态的编码。我们将证明“提升”转换不会影响这种关系。最后,我们将提供一些经验结果,这些结果表明相对编码大小确实与求解它们的难度相关。最后,我们讨论传统的计划空间计划者相对于状态空间计划者而言的首要地位何时可以延续到各自的SAT编码中。

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