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Artificial Intelligence : A Modern Approach
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ISBN
:
9788177583670
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Publisher
:
Pearson
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Subject
:
Computer Science
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699.0
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545.0
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Description
For one or two-semester,undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Salent Features NEW—Nontechnical learning material-provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. NEW—Nontechnical learning material-provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. NEW—Nontechnical learning material-provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. NEW—The Internet as a sample application for intelligent systems—Examples of logical reasoning, planning, and natural language processing using Internet agents. NEW—Increased coverage of material—New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics. NEW—More Online Software. A unified, agent-based approach to AI—Organizes the material around the task of building intelligent agents. Comprehensive, up-to-date coverage—Includes a unified view of the field organized around the rational decision making paradigm. A flexible format-makes the text adaptable for varying instructors' preferences. In-depth coverage of basic and advanced topics. Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet. Table Of ContentsARTIFICIAL INTELLIGENCE. Introduction. Intelligent Agents. Solving Problems by Searching. Informed Search and Exploration. Constraint Satisfaction Problems. Adversarial Search.KNOWLEDGE AND REASONING. Logical Agents. First-Order Logic. Inference in First-Order Logic. Knowledge Representation. Planning. Planning and Acting in the Read World. UNCERTAIN KNOWLEDGE AND REASONING. Uncertainty. Probabilistic Reasoning Systems. Probabilistic Reasoning Over Time. Making Simple Decisions. Making Complex Decisions. LEARNING. Learning from Observations. Statistical Learning. Reinforcement Learning. Knowledge in Learning. COMMUNICATING, PERCEIVING, AND ACTING. Agents that Communicate. Text Processing in the Large. Perception. Robotics. CONCLUSIONS. Philosophical Foundations. AI: Present and Future.
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