| 書(shū)目名稱(chēng) | Representing Uncertain Knowledge | | 副標(biāo)題 | An Artificial Intell | | 編輯 | Paul Krause,Dominic Clark | | 視頻video | http://file.papertrans.cn/828/827532/827532.mp4 | | 圖書(shū)封面 |  | | 描述 | The representation of uncertainty is a central issue in Artificial Intelligence (AI) and is being addressed in many different ways. Each approach has its proponents, and each has had its detractors. However, there is now an in- creasing move towards the belief that an eclectic approach is required to represent and reason under the many facets of uncertainty. We believe that the time is ripe for a wide ranging, yet accessible, survey of the main for- malisms. In this book, we offer a broad perspective on uncertainty and approach- es to managing uncertainty. Rather than provide a daunting mass of techni- cal detail, we have focused on the foundations and intuitions behind the various schools. The aim has been to present in one volume an overview of the major issues and decisions to be made in representing uncertain knowl- edge. We identify the central role of managing uncertainty to AI and Expert Systems, and provide a comprehensive introduction to the different aspects of uncertainty. We then describe the rationales, advantages and limitations of the major approaches that have been taken, using illustrative examples. The book ends with a review of the lessons learned and current res | | 出版日期 | Book 1993 | | 關(guān)鍵詞 | Factor; argumentation; artificial intelligence; computer science; design; expert system; intelligence; know | | 版次 | 1 | | doi | https://doi.org/10.1007/978-94-011-2084-5 | | isbn_softcover | 978-94-010-4925-2 | | isbn_ebook | 978-94-011-2084-5 | | copyright | Springer Science+Business Media Dordrecht 1993 |
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