{"title":"一种基于自配置群的空间探索系统的形式化方法","authors":"Emil Vassev, M. Hinchey, P. Nixon","doi":"10.1109/AHS.2010.5546276","DOIUrl":null,"url":null,"abstract":"Intelligent swarms draw their inspiration from biology where many simple entities act independently, but when grouped, they appear to be highly organized. NASA is currently investigating swarm-based technologies for the development of prospective exploration missions to explore regions of space where a single large spacecraft would be impractical. The main emphasis of this research is to develop algorithms and prototyping models for self-managing swarm-based space-exploration systems. This article presents our work on formally modeling self-configuring behavior in such systems. We present a formal model for team formation based on Partially Observable Markov Decision Processes and Discrete Time Markov Chains along with formal models for planning and scheduling.","PeriodicalId":101655,"journal":{"name":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2010-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"A formal approach to self-configurable swarm-based space-exploration systems\",\"authors\":\"Emil Vassev, M. Hinchey, P. Nixon\",\"doi\":\"10.1109/AHS.2010.5546276\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Intelligent swarms draw their inspiration from biology where many simple entities act independently, but when grouped, they appear to be highly organized. NASA is currently investigating swarm-based technologies for the development of prospective exploration missions to explore regions of space where a single large spacecraft would be impractical. The main emphasis of this research is to develop algorithms and prototyping models for self-managing swarm-based space-exploration systems. This article presents our work on formally modeling self-configuring behavior in such systems. We present a formal model for team formation based on Partially Observable Markov Decision Processes and Discrete Time Markov Chains along with formal models for planning and scheduling.\",\"PeriodicalId\":101655,\"journal\":{\"name\":\"2010 NASA/ESA Conference on Adaptive Hardware and Systems\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 NASA/ESA Conference on Adaptive Hardware and Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/AHS.2010.5546276\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 NASA/ESA Conference on Adaptive Hardware and Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AHS.2010.5546276","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A formal approach to self-configurable swarm-based space-exploration systems
Intelligent swarms draw their inspiration from biology where many simple entities act independently, but when grouped, they appear to be highly organized. NASA is currently investigating swarm-based technologies for the development of prospective exploration missions to explore regions of space where a single large spacecraft would be impractical. The main emphasis of this research is to develop algorithms and prototyping models for self-managing swarm-based space-exploration systems. This article presents our work on formally modeling self-configuring behavior in such systems. We present a formal model for team formation based on Partially Observable Markov Decision Processes and Discrete Time Markov Chains along with formal models for planning and scheduling.