Introduction to Artificial Intelligence

Introduction to Artificial Intelligence

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This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website.The total computation time for all 7.4A— 1073 inferences would be approximately equal to 7.4A— 1073 inferences 10000A— 109 inferences/sec = 7.4A— 1060 sec ... In chess for example, there are over 30 possible moves for a typical situation, and a game lasting 50 half-turns is relatively short. ... Example 6.1 With the 8-puzzle, a classic example for search algorithms [Nil98, RN10], the various algorithms can beanbsp;...


Title:Introduction to Artificial Intelligence
Author: Nathanael T. Black, Wolfgang Ertel
Publisher:Springer Science & Business Media - 2011-03-18
ISBN-13:

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