{"title":"a#:用于因子规划的A*的分布式版本","authors":"L. Jezequel, É. Fabre","doi":"10.1109/CDC.2012.6426187","DOIUrl":null,"url":null,"abstract":"Factored planning consists in driving a modular or distributed system to a target state, in an optimal manner, assuming all actions are controllable. Such problems take the form of path search in a product of graphs. The state space of each component is a graph, in which one must find a path to the local goal of this component. But when all components are considered jointly, the problem amounts to finding a path in each of these state graphs, while ensuring their compatibility on the actions that must be performed jointly by some components of the system. This paper proposes a solution under the form of a multi-agent version of A*, assembling several A*, each one performing a biased depth-first search in the graph of each component.","PeriodicalId":312426,"journal":{"name":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2012-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":"{\"title\":\"A#: A distributed version of A* for factored planning\",\"authors\":\"L. Jezequel, É. Fabre\",\"doi\":\"10.1109/CDC.2012.6426187\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Factored planning consists in driving a modular or distributed system to a target state, in an optimal manner, assuming all actions are controllable. Such problems take the form of path search in a product of graphs. The state space of each component is a graph, in which one must find a path to the local goal of this component. But when all components are considered jointly, the problem amounts to finding a path in each of these state graphs, while ensuring their compatibility on the actions that must be performed jointly by some components of the system. This paper proposes a solution under the form of a multi-agent version of A*, assembling several A*, each one performing a biased depth-first search in the graph of each component.\",\"PeriodicalId\":312426,\"journal\":{\"name\":\"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)\",\"volume\":\"52 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"12\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/CDC.2012.6426187\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2012 IEEE 51st IEEE Conference on Decision and Control (CDC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CDC.2012.6426187","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A#: A distributed version of A* for factored planning
Factored planning consists in driving a modular or distributed system to a target state, in an optimal manner, assuming all actions are controllable. Such problems take the form of path search in a product of graphs. The state space of each component is a graph, in which one must find a path to the local goal of this component. But when all components are considered jointly, the problem amounts to finding a path in each of these state graphs, while ensuring their compatibility on the actions that must be performed jointly by some components of the system. This paper proposes a solution under the form of a multi-agent version of A*, assembling several A*, each one performing a biased depth-first search in the graph of each component.