{"title":"多状态承诺搜索","authors":"Y. Kitamura, M. Yokoo, T. Miyaji, S. Tatsumi","doi":"10.1109/TAI.1998.744882","DOIUrl":null,"url":null,"abstract":"We propose the multi-state commitment (MSC) method to speed-up heuristic search algorithms for semi-optimal solutions. The real-time A* (RTA*) and the weighted A* (WA*) are representative heuristic search algorithms for semi-optimal solutions and can be viewed as single-state and an all-state commitment search algorithms respectively. In these algorithms, there is a tradeoff between the risk of making wrong choices in search process and the amount of memory for the recovery, with RTA* and WA* being the extremes. The MSC method introduces a moderate and flexible characteristic into these algorithms and can increase the performance dramatically in problems such as the N-puzzle. In this paper, by introducing a commitment-list, we show a modification of RTA* and WA* to their MSC versions without violating their completeness. Then, we experiment with their performance in maze and N-puzzle problems, and discuss conditions that the MSC method is effective.","PeriodicalId":424568,"journal":{"name":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Multi-state commitment search\",\"authors\":\"Y. Kitamura, M. Yokoo, T. Miyaji, S. Tatsumi\",\"doi\":\"10.1109/TAI.1998.744882\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"We propose the multi-state commitment (MSC) method to speed-up heuristic search algorithms for semi-optimal solutions. The real-time A* (RTA*) and the weighted A* (WA*) are representative heuristic search algorithms for semi-optimal solutions and can be viewed as single-state and an all-state commitment search algorithms respectively. In these algorithms, there is a tradeoff between the risk of making wrong choices in search process and the amount of memory for the recovery, with RTA* and WA* being the extremes. The MSC method introduces a moderate and flexible characteristic into these algorithms and can increase the performance dramatically in problems such as the N-puzzle. In this paper, by introducing a commitment-list, we show a modification of RTA* and WA* to their MSC versions without violating their completeness. Then, we experiment with their performance in maze and N-puzzle problems, and discuss conditions that the MSC method is effective.\",\"PeriodicalId\":424568,\"journal\":{\"name\":\"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)\",\"volume\":\"6 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/TAI.1998.744882\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings Tenth IEEE International Conference on Tools with Artificial Intelligence (Cat. No.98CH36294)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/TAI.1998.744882","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
We propose the multi-state commitment (MSC) method to speed-up heuristic search algorithms for semi-optimal solutions. The real-time A* (RTA*) and the weighted A* (WA*) are representative heuristic search algorithms for semi-optimal solutions and can be viewed as single-state and an all-state commitment search algorithms respectively. In these algorithms, there is a tradeoff between the risk of making wrong choices in search process and the amount of memory for the recovery, with RTA* and WA* being the extremes. The MSC method introduces a moderate and flexible characteristic into these algorithms and can increase the performance dramatically in problems such as the N-puzzle. In this paper, by introducing a commitment-list, we show a modification of RTA* and WA* to their MSC versions without violating their completeness. Then, we experiment with their performance in maze and N-puzzle problems, and discuss conditions that the MSC method is effective.