重新审视有限域状态变量规划中的分块去序问题

IF 1.4 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE AI Communications Pub Date : 2024-04-03 DOI:10.3233/aic-230058
Sabah Binte Noor, Fazlul Hasan Siddiqui
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引用次数: 0

摘要

计划排序消除了计划中行动之间不必要的排序约束,有利于计划的灵活执行和其他一些任务,如计划的重用、修改和分解。区块排序是计划排序的一种变体,它将连贯的行动封装成区块,以消除部分排序计划(POP)中的进一步排序约束,在许多应用中(如生成宏行动和提高整体计划质量)都很有用。现有的分块去序策略采用命题编码。与命题编码相比,有限域状态变量编码(如 SAS+ 表示法)可以通过因果图(CG)和域转换图(DTG)等简洁的结构来捕捉规划实例的内部结构和状态变量行为。这项工作在有限域表示(FDR)中重新定义了块排序术语和相关计划排序概念的语义。我们提出的语义还解决了现有块语义的一些局限性,并进一步提高了计划的灵活性。此外,这项工作还利用块去序来消除持久性有机污染物中的冗余操作。我们还使用基于解释的顺序泛化(EOG)和 MaxSAT 对块去序法与各种去序/重排技术进行了比较分析。我们在国际规划竞赛(IPC)的基准问题上进行的实验表明,我们的分块排序 FDR 形式显著提高了计划执行的灵活性,同时保持了良好的覆盖率和执行时间。
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Revisiting block deordering in finite-domain state variable planning
Plan deordering removes unnecessary ordering constraints between actions in a plan, facilitating plan execution flexibility and several other tasks, such as plan reuse, modification, and decomposition. Block deordering is a variant of plan deordering that encapsulates coherent actions into blocks to eliminate further ordering constraints from a partial-order plan (POP) and is useful in many applications (e.g., generating macro-actions and improving the overall plan quality). The existing block deordering strategy is formulated in propositional encodings. Finite-domain state variable encodings (e.g., SAS+ representation), in contrast with propositional encodings, can capture the internal structure and the behavior of state variables of a planning instance through concise constructs such as causal graphs (CGs) and domain transition graphs (DTGs). This work redefines the semantics of block deordering terminologies and related plan deordering concepts in finite domain representation (FDR). Our proposed semantics also resolves some limitations of the existing block semantics and further enhance plan flexibility. In addition, this work exploits block deordering to eliminate redundant actions from a POP. A comparative analysis is also performed on block deordering with various deordering/reordering techniques using explanation-based order generalization (EOG) and MaxSAT. Our experiments on the benchmark problems from International Planning Competitions (IPC) show that our FDR formalism of block deordering significantly improves the plan execution flexibility while maintaining good coverage and execution time.
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来源期刊
AI Communications
AI Communications 工程技术-计算机:人工智能
CiteScore
2.30
自引率
12.50%
发文量
34
审稿时长
4.5 months
期刊介绍: AI Communications is a journal on artificial intelligence (AI) which has a close relationship to EurAI (European Association for Artificial Intelligence, formerly ECCAI). It covers the whole AI community: Scientific institutions as well as commercial and industrial companies. AI Communications aims to enhance contacts and information exchange between AI researchers and developers, and to provide supranational information to those concerned with AI and advanced information processing. AI Communications publishes refereed articles concerning scientific and technical AI procedures, provided they are of sufficient interest to a large readership of both scientific and practical background. In addition it contains high-level background material, both at the technical level as well as the level of opinions, policies and news.
期刊最新文献
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