Planning strategy representation in dolittle

研究成果: 書貢獻/報告類型會議論文篇章

摘要

This paper introduces multi-strategy planning and describes its implementation in the DOLITTLE system, which can combine many different planning strategies, including means-ends analysis, macro-based planning, abstraction-based planning (reduced and relaxed), and casebased planning on a single problem. Planning strategies are defined as methods to reduce the search space by exploiting some assumptions (socalled planning biases) about the problem domain. General operators are generalizations of standard STRIPS operators that conveniently represent many different planning strategies. The focus of this work is to develop a representation weak enough to represent a wide variety of different strategies, but still strong enough to emulate them. The search control method applies different general operators based on a strongest first principle; planning biases that are expected to lead to small search spaces are tried first. An empirical evaluation in three domains showed that multi-strategy planning performed significantly better than the best single strategy planners in these domains.

原文英語
主出版物標題Advances in Artificial Intelligence - 12th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 1998, Proceedings
編輯Robert E. Mercer, Eric Neufeld
發行者Springer Verlag
頁面30-44
頁數15
ISBN(列印)3540645756, 9783540645757
DOIs
出版狀態已發佈 - 1998 一月 1
事件12th Biennial Conference on Artificial Intelligence, AI 1998 - Vancouver, 加拿大
持續時間: 1998 六月 181998 六月 20

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
1418
ISSN(列印)0302-9743
ISSN(電子)1611-3349

其他

其他12th Biennial Conference on Artificial Intelligence, AI 1998
國家加拿大
城市Vancouver
期間1998/06/181998/06/20

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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