{"title":"三维多人避险游戏的最佳策略与合作组队","authors":"Peng Gao;Xiuxian Li;Jinwen Hu","doi":"10.1109/TCDS.2024.3406889","DOIUrl":null,"url":null,"abstract":"This article studies multiplayer reach-avoid games with a plane being the goal in 3-D space. Due to the difficulty that directly analyzing multipursuer multievader scenarios brings the curse of dimensionality, the whole problem is decomposed to distinct subgames. In the subgames, a single pursuer or multiple pursuers, which have different speeds, form a team to capture one evader cooperatively while the evader struggles to reach the plane. With the players’ dominance region based on the definition of isochronous surfaces, the target points and value functions are obtained for the game of degree by using Apollonius spheres. Additionally, the corresponding closed-loop saddle-point strategies are shown to be Nash equilibrium. The degeneration between scenarios of different scales is also discussed. To minimize the sum of subgames’ costs, the tasks of intercepting multiple evaders are assigned to individuals or teams in the form of bipartite graph matching. A hierarchical matching algorithm and a state-feedback rematching method are proposed which can be updated in real-time to improve the solution. Finally, diverse empirical experiments and comparisons with state-of-the-art methods are illustrated to demonstrate the optimality of proposed strategies and algorithms in this article.","PeriodicalId":54300,"journal":{"name":"IEEE Transactions on Cognitive and Developmental Systems","volume":"16 6","pages":"2085-2099"},"PeriodicalIF":5.0000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Strategies and Cooperative Teaming for 3-D Multiplayer Reach-Avoid Games\",\"authors\":\"Peng Gao;Xiuxian Li;Jinwen Hu\",\"doi\":\"10.1109/TCDS.2024.3406889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article studies multiplayer reach-avoid games with a plane being the goal in 3-D space. Due to the difficulty that directly analyzing multipursuer multievader scenarios brings the curse of dimensionality, the whole problem is decomposed to distinct subgames. In the subgames, a single pursuer or multiple pursuers, which have different speeds, form a team to capture one evader cooperatively while the evader struggles to reach the plane. With the players’ dominance region based on the definition of isochronous surfaces, the target points and value functions are obtained for the game of degree by using Apollonius spheres. Additionally, the corresponding closed-loop saddle-point strategies are shown to be Nash equilibrium. The degeneration between scenarios of different scales is also discussed. To minimize the sum of subgames’ costs, the tasks of intercepting multiple evaders are assigned to individuals or teams in the form of bipartite graph matching. A hierarchical matching algorithm and a state-feedback rematching method are proposed which can be updated in real-time to improve the solution. Finally, diverse empirical experiments and comparisons with state-of-the-art methods are illustrated to demonstrate the optimality of proposed strategies and algorithms in this article.\",\"PeriodicalId\":54300,\"journal\":{\"name\":\"IEEE Transactions on Cognitive and Developmental Systems\",\"volume\":\"16 6\",\"pages\":\"2085-2099\"},\"PeriodicalIF\":5.0000,\"publicationDate\":\"2024-06-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Cognitive and Developmental Systems\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10545356/\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Cognitive and Developmental Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10545356/","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Optimal Strategies and Cooperative Teaming for 3-D Multiplayer Reach-Avoid Games
This article studies multiplayer reach-avoid games with a plane being the goal in 3-D space. Due to the difficulty that directly analyzing multipursuer multievader scenarios brings the curse of dimensionality, the whole problem is decomposed to distinct subgames. In the subgames, a single pursuer or multiple pursuers, which have different speeds, form a team to capture one evader cooperatively while the evader struggles to reach the plane. With the players’ dominance region based on the definition of isochronous surfaces, the target points and value functions are obtained for the game of degree by using Apollonius spheres. Additionally, the corresponding closed-loop saddle-point strategies are shown to be Nash equilibrium. The degeneration between scenarios of different scales is also discussed. To minimize the sum of subgames’ costs, the tasks of intercepting multiple evaders are assigned to individuals or teams in the form of bipartite graph matching. A hierarchical matching algorithm and a state-feedback rematching method are proposed which can be updated in real-time to improve the solution. Finally, diverse empirical experiments and comparisons with state-of-the-art methods are illustrated to demonstrate the optimality of proposed strategies and algorithms in this article.
期刊介绍:
The IEEE Transactions on Cognitive and Developmental Systems (TCDS) focuses on advances in the study of development and cognition in natural (humans, animals) and artificial (robots, agents) systems. It welcomes contributions from multiple related disciplines including cognitive systems, cognitive robotics, developmental and epigenetic robotics, autonomous and evolutionary robotics, social structures, multi-agent and artificial life systems, computational neuroscience, and developmental psychology. Articles on theoretical, computational, application-oriented, and experimental studies as well as reviews in these areas are considered.