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Titlebook: Knowledge Representation Techniques; A Rough Set Approach Patrick Doherty,Witold ?ukaszewicz,Andrzej Sza?as Book 2006 Springer-Verlag Berli

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書目名稱Knowledge Representation Techniques
副標(biāo)題A Rough Set Approach
編輯Patrick Doherty,Witold ?ukaszewicz,Andrzej Sza?as
視頻videohttp://file.papertrans.cn/545/544021/544021.mp4
概述Most important feature of our book is a new methodology and new methods for approximate reasoning leading from experimental knowledge (e.g. sensor measurements) to conclusions in natural language. The
叢書名稱Studies in Fuzziness and Soft Computing
圖書封面Titlebook: Knowledge Representation Techniques; A Rough Set Approach Patrick Doherty,Witold ?ukaszewicz,Andrzej Sza?as Book 2006 Springer-Verlag Berli
描述1. 1 Background The basis for the material in this book centers around research done in an ongoing long-term project which focuses on the development of highly au- 1 tonomousunmannedaerialvehiclesystems. Theactualplatformwhichserves as a case study for the research in this book will be described in detail later in this chapter. Before doing that, a brief background of the motivations - hind this research will be provided. One of the main research topics in the project is knowledge representation and reasoning and its use in Uav pl- forms. A very strong constraint has been placed on the nature of research done in the project where theoretical results, to the greatest extent possible, should serve as a basis for tractable reasoning mechanisms for use in a fully deployed autonomous Uav operating under soft real-time constraints asso- ated with the types of mission scenarios envisioned. Considering that much of the work with knowledge representation in this context focuses on application domains where one can only hope for an incomplete characterization of such domains, this methodological constraint has proven to be quite challenging since, in essence, the focus is on tractable approx
出版日期Book 2006
關(guān)鍵詞CAKE: Computer Aided Knowledge Engineering; Knowledge Representation; Rough Knowledge Databases; Sensor
版次1
doihttps://doi.org/10.1007/3-540-33519-6
isbn_softcover978-3-642-07012-9
isbn_ebook978-3-540-33519-1Series ISSN 1434-9922 Series E-ISSN 1860-0808
issn_series 1434-9922
copyrightSpringer-Verlag Berlin Heidelberg 2006
The information of publication is updating

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Rough Setsting and processing incomplete, imprecise, and approximate data. Many of these applications require the use of approximate reasoning techniques. Before we introduce rough sets formally, let us begin with an intuitive example where representation of approximate data and reasoning with it is an essential component in the modeling process.
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Non-Monotonic ReasoningTraditional logics are monotonic, i.e., adding new premises (axioms) will never invalidate previously inferred conclusions (theorems), or, equivalently, the set of conclusions non-decreases monotonically with the set of premises. Formally, a logic is monotonic if and only if it satisfies the condition that for any sets of premises . and .,
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A UAV Scenario: A Case StudyIn the current chapter we provide a small case study, based on the Witas Uav application domain to illustrate various knowledge representation and reasoning techniques presented in the book.
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978-3-642-07012-9Springer-Verlag Berlin Heidelberg 2006
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Knowledge Representation Techniques978-3-540-33519-1Series ISSN 1434-9922 Series E-ISSN 1860-0808
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https://doi.org/10.1007/3-540-33519-6CAKE: Computer Aided Knowledge Engineering; Knowledge Representation; Rough Knowledge Databases; Sensor
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