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Titlebook: Operations Research and Artificial Intelligence: The Integration of Problem-Solving Strategies; Donald E. Brown,Chelsea C. White Book 1990

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書(shū)目名稱(chēng)Operations Research and Artificial Intelligence: The Integration of Problem-Solving Strategies
編輯Donald E. Brown,Chelsea C. White
視頻videohttp://file.papertrans.cn/703/702134/702134.mp4
圖書(shū)封面Titlebook: Operations Research and Artificial Intelligence: The Integration of Problem-Solving Strategies;  Donald E. Brown,Chelsea C. White Book 1990
描述The purpose of this book is to introduce and explain research at the boundary between two fields that view problem solving from different perspectives. Researchers in operations research and artificial intelligence have traditionally remained separate in their activities. Recently, there has been an explosion of work at the border of the two fields, as members of both communities seek to leverage their activities and resolve problems that remain intractable to pure operations research or artificial intelligence techniques. This book presents representative results from this current flurry of activity and provides insights into promising directions for continued exploration. This book should be of special interest to researchers in artificial intelligence and operations research because it exposes a number of applications and techniques, which have benefited from the integration of problem solving strategies. Even researchers working on different applications or with different techniques can benefit from the descriptions contained here, because they provide insight into effective methods for combining approaches from the two fields. Additionally, researchers in both communities will
出版日期Book 1990
關(guān)鍵詞Fuzzy; Operations Research; algorithms; artificial intelligence; automated reasoning; calculus; complexity
版次1
doihttps://doi.org/10.1007/978-94-009-2203-7
isbn_softcover978-94-010-7488-9
isbn_ebook978-94-009-2203-7
copyrightKluwer Academic Publishers 1990
The information of publication is updating

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Discovering and Refining Algorithms Through Machine Learnings to discover and refine algorithms for simple scheduling problems. The discovered algorithms can be considered to be rule bases that are modified and adapted through training with examples. The quality of the resultant algorithm is investigated as a function of the training.
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Introduction the natural evolution of both fields toward more powerful strategies of problem solving. These strategies will find usefulness for both decision aiding, a goal of OR, and automatic decision making, which is the pursuit of AI.
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Book 1990. Researchers in operations research and artificial intelligence have traditionally remained separate in their activities. Recently, there has been an explosion of work at the border of the two fields, as members of both communities seek to leverage their activities and resolve problems that remain
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Eliciting Knowledge Representation Schema for Linear Programming Formulationthis area elicits methods only, leaving the KR schema up to the Knowledge Engineer’s discretion. We argue that a more “natural” system can be developed if we know more about expert KR schema, and describe a study to elicit these schema
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