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Mathematical Methods in Artificial Intelligence (Practitioners)
por Edward A. Bender
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Mathematical Methods in Artificial Intelligence introduces thestudent to the important mathematical foundations and tools in AIand describes their applications to the design of AI algorithms.This useful text presents an introductory AI course based on themost important mathematics and its applications. It focuses onimportant topics that are proven useful in AI and involve the mostbroadly applicable mathematics. The book explores AI from three different viewpoints: goals,methods or tools, and achievements and failures. Its goals ofreasoning, planning, learning, or language understanding and useare centered around the expert system idea. The tools of AI arepresented in terms of what can be incorporated in the datastructures. The book looks into the concepts and tools of limitedstructure, mathematical logic, logic-like representation, numericalinformation, and nonsymbolic structures. The text emphasizes the main mathematical tools for representingand manipulating knowledge symbolically. These are various forms oflogic for qualitative knowledge, and probability and relatedconcepts for quantitative knowledge. The main tools formanipulating knowledge nonsymbolically, as neural nets, areoptimization methods and statistics. This material is covered inthe text by topics such as trees and search, classical mathematicallogic, and uncertainty and reasoning. A solutions diskette isavailable, please call for more information.
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Melvil Decimal System (DDC)006.3015 — Information Computing and Information Special Topics Artificial Intelligence
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