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The study proves and verifies that policies outperform plans across more instance types and yield better solutions.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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does MAPF-TU extend, and what uncertainty is introduced?","Question",{"text":63,"@type":64},"MAPF-TU extends Multi-Agent Path Finding by allowing each move action’s duration to be uncertain within a given time interval range.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the proposed approach ensure safety for all agents?",{"text":68,"@type":64},"It generates policies that guarantee each agent reaches its destination without collisions with others, assuming every agent stays within its predefined temporal uncertainty range.",{"name":70,"@type":61,"acceptedAnswer":71},"What is the key advantage of using policies instead of plans?",{"text":72,"@type":64},"The paper shows both theoretically and empirically that policies solve more types of instances and produce better overall solutions than using fixed 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TemporalUncertainty\n\nJiříŠvancara¹D,David Zahrádka²,3Db,Mrinalini Subramanian¹DC,  \nRoman Barták¹Dd and Miroslav Kulich³De  \nFaculty of Mathematics and Physics,Charles University,Prague,Czech Republic  \n²Dept.of Cybernetics,Faculty of Electrical Engineering,Czech Technical University in Prague,Prague,Czech Republic3Czech Institute of Informatics,Robotics and Cybernetics,Czech Technical University in Prague,Prague,Czech Republic  \nKeywords:  \nMulti-Agent Path Finding,Temporal Uncertainty,Policy,SAT.  \nAbstract:Multi-Agent path finding(MAPF)deals with the problem of finding collision-free paths for a group of mobileagents moving in a shared environment.In practice,the duration of individual move actions may not be exactbut rather spans in a given range.Such extension of the MAPF problem is called MAPF with TemporalUncertainty(MAPF-TU).In this paper,we propose a compilation-based approach to generate safe agents'policies solving the MAPF-TU problem.The policy guarantees that each agent reaches its destination withoutcollision with other agents provided that all agents move within their predefined temporal uncertainty range.We show both theoretically and empirically that using policies rather than plans is guaranteed to solve moretypes of instances and find a better solution.  \n## 1 INTRODUCTION\n\ncles.In the original paper (Shahar et al.,2021),theauthors defined the problem and presented an optimalalgorithm based on the Conflict-Based Search(CBS)algorithm(Sharon et al.,2015).Specifically,they as-sumed blind execution of the found plan and requiredthat the plan be safe(i.e.collision-free)for any dura-tion of the traversal.  \nCoordinating a fleet of moving agents is an impor-tant problem with practical applications such as ware-housing(Wurman et al.,2008),airplane taxing(Mor-ris et al.,2015),or traffic junctions(Dresner andStone,2008).Multi-Agent Path Finding(MAPF)isan abstract model of this coordination problem,wherewe are looking for collision-free paths for agentsmoving in a shared environment represented by agraph.The problem of finding an optimal MAPF so-lution has been shown to be NP-hard for a wide rangeof cost objectives(Surynek,2010).  \nIn this paper,we extend the concept of MAPF-TUto include different actions depending on the traversaltime,thus,creating apolicy.This extension allows theagent to move more efficiently and possibly reach thegoal faster,yet still guarantees safety.A reduction-based model is introduced that facilitates policy cre-ation.Using a reduction-based approach is quite nat-ural as the variables modeling the policy are alreadypresent in the model for classical MAPF.Example in-stances are provided to demonstrate the benefit of us-ing policies instead of plans.Also,theoretical guaran-tees on the solution's existence and the length of thesolution are provided.Lastly,empirical experimentsare carried out to compare the benefits of policies interms of the length of the final plan.  \nA practical extension of the classical MAPF prob-lem is MAPF with Temporal Uncertainty (MAPF-TU)(Shahar et al.,2021),where each transition be-tween two locations is associated with a lower and up-per bound on the time it takes any agent to move.Thisextension models the real world,where the movementof agents may be affected by terrain,imperfect execu-tion,uncertainty in localization,or unexpected obsta-  \naDhttps://orcid.org/0000-0002-6275-6773  \nbDhttps://orcid.org/0000-0002-7380-8495  \ncDhttps://orcid.org/0009-0002-9511-9217  \ndDhttps://orcid.org/0000-0002-6717-8175  \neDhttps://orcid.org/0000-0002-0997-5889  \n1238  \n## 2 RELATED WORK\n\nConflict Based Search(CBS),a search-based solver,is a complete and optimal MAPF solver designedto solve MAPF problems by iteratively planning foreach agent individually on the lower level.A top-levelsearch over a constraint tree is performed to identifyconflicts between the single-agent paths and resolvethem(Sharon et al,2015).  \nA reductio","cbCaioGCiJP4uGx1","https://ap.wps.com/l/cbCaioGCiJP4uGx1","pdf",590481,"English","# 1 Introduction\n# 2 Related Work\n# 3 Problem Definition and Properties","[{\"question\":\"What problem does MAPF-TU extend, and what uncertainty is introduced?\",\"answer\":\"MAPF-TU extends Multi-Agent Path Finding by allowing each move action’s duration to be uncertain within a given time interval range.\"},{\"question\":\"How does the proposed approach ensure safety for all agents?\",\"answer\":\"It generates policies that guarantee each agent reaches its destination without collisions with others, assuming every agent stays within its predefined temporal uncertainty range.\"},{\"question\":\"What is the key advantage of using policies instead of plans?\",\"answer\":\"The paper shows both theoretically and empirically that policies solve more types of instances and produce better overall solutions than using fixed plans.\"}]","Generating Safe Policies for Multi-Agent Path Finding with TemporalUncertainty | PDF",1791091083,18]