{"title":"Revisiting block deordering in finite-domain state variable planning","authors":"Sabah Binte Noor, Fazlul Hasan Siddiqui","doi":"10.3233/aic-230058","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":50835,"journal":{"name":"AI Communications","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"AI Communications","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.3233/aic-230058","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
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.
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.