{"title":"用于多代理路径查找的反例引导抽象细化中的非定义抽象(扩展摘要)","authors":"Pavel Surynek","doi":"10.1609/socs.v17i1.31584","DOIUrl":null,"url":null,"abstract":"Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that agents do not collide with each other. The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process.","PeriodicalId":425645,"journal":{"name":"Symposium on Combinatorial Search","volume":"10 4","pages":"287-288"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Non-Refined Abstractions in Counterexample Guided Abstraction Refinement for Multi-Agent Path Finding (Extended Abstract)\",\"authors\":\"Pavel Surynek\",\"doi\":\"10.1609/socs.v17i1.31584\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that agents do not collide with each other. The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process.\",\"PeriodicalId\":425645,\"journal\":{\"name\":\"Symposium on Combinatorial Search\",\"volume\":\"10 4\",\"pages\":\"287-288\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Symposium on Combinatorial Search\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1609/socs.v17i1.31584\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Symposium on Combinatorial Search","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/socs.v17i1.31584","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
摘要
反例引导抽象提炼(CEGAR)是一种强大的符号技术,适用于各种任务,如模型检查和可达性分析。最近,CEGAR 与布尔可满足性(SAT)相结合,被应用于多代理路径查找(MAPF)问题,该问题的任务是将代理从其起始位置导航到给定的各个目标位置,使代理之间不会发生碰撞。最近的 CEGAR 方法使用了 MAPF 问题的初始抽象,其中省略了代理之间的碰撞,并在随后的抽象细化中消除了碰撞。在本研究中,我们提出了一种基于 SAT 的新颖 CEGAR 式 MAPF 求解器,其中一些抽象被刻意忽略。这就增加了对从底层 SAT 求解器获得的答案进行后处理的必要性,因为这些答案与正确的 MAPF 解决方案略有不同。不过,与前一种方法相比,不精炼会产生数量级更小的 SAT 编码,并加快整个求解过程。
Non-Refined Abstractions in Counterexample Guided Abstraction Refinement for Multi-Agent Path Finding (Extended Abstract)
Counterexample guided abstraction refinement (CEGAR) represents a powerful symbolic technique for various tasks such as model checking and reachability analysis. Recently, CEGAR combined with Boolean satisfiability (SAT) has been applied for multi-agent path finding (MAPF), a problem where the task is to navigate agents from their start positions to given individual goal positions so that agents do not collide with each other. The recent CEGAR approach used the initial abstraction of the MAPF problem where collisions between agents were omitted and were eliminated in subsequent abstraction refinements. We propose in this work a novel CEGAR-style solver for MAPF based on SAT in which some abstractions are deliberately left non-refined. This adds the necessity to post-process the answers obtained from the underlying SAT solver as these answers slightly differ from the correct MAPF solutions. Non-refining however yields order-of-magnitude smaller SAT encodings than those of the previous approach and speeds up the overall solving process.