{"title":"Approximating the Value of Collaborative Team Actions for Efficient Multiagent Navigation in Uncertain Graphs","authors":"M. Stadler, Jacopo Banfi, Nicholas A. Roy","doi":"10.1609/icaps.v33i1.27250","DOIUrl":null,"url":null,"abstract":"For a team of collaborative agents navigating through an unknown environment, collaborative actions such as sensing the traversability of a route can have a large impact on aggregate team performance. However, planning over the full space of joint team actions is generally computationally intractable. Furthermore, typically only a small number of collaborative actions is useful for a given team task, but it is not obvious how to assess the usefulness of a given action. In this work, we model collaborative team policies on stochastic graphs using macro-actions, where each macro-action for a given agent can consist of a sequence of movements, sensing actions, and actions of waiting to receive information from other agents. To reduce the number of macro-actions considered during planning, we generate optimistic approximations of candidate future team states, then restrict the planning domain to a small policy class which consists of only macro-actions which are likely to lead to high-reward future team states. We optimize team plans over the small policy class, and demonstrate that the approach enables a team to find policies which actively balance between reducing task-relevant environmental uncertainty and efficiently navigating to goals in toy graph and island road network domains, finding better plans than policies that do not act to reduce environmental uncertainty.","PeriodicalId":239898,"journal":{"name":"International Conference on Automated Planning and Scheduling","volume":"382 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Automated Planning and Scheduling","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1609/icaps.v33i1.27250","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
For a team of collaborative agents navigating through an unknown environment, collaborative actions such as sensing the traversability of a route can have a large impact on aggregate team performance. However, planning over the full space of joint team actions is generally computationally intractable. Furthermore, typically only a small number of collaborative actions is useful for a given team task, but it is not obvious how to assess the usefulness of a given action. In this work, we model collaborative team policies on stochastic graphs using macro-actions, where each macro-action for a given agent can consist of a sequence of movements, sensing actions, and actions of waiting to receive information from other agents. To reduce the number of macro-actions considered during planning, we generate optimistic approximations of candidate future team states, then restrict the planning domain to a small policy class which consists of only macro-actions which are likely to lead to high-reward future team states. We optimize team plans over the small policy class, and demonstrate that the approach enables a team to find policies which actively balance between reducing task-relevant environmental uncertainty and efficiently navigating to goals in toy graph and island road network domains, finding better plans than policies that do not act to reduce environmental uncertainty.