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Artifical intelligence : structures and strategies for complex problem solving

In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence-solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: "AI Algorithms in Prolog, Lisp and Java (TM). "References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.

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  • "复杂问题求解的结构和策略"
  • "Fu za wen ti qiu jie de jie gou he ce lüe"

http://schema.org/description

  • "In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence-solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: "AI Algorithms in Prolog, Lisp and Java (TM). "References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence."
  • "In this accessible, comprehensive text, George Luger captures the essence of artificial intelligence-solving the complex problems that arise wherever computer technology is applied. Key representation techniques including logic, semantic and connectionist networks, graphical models, and many more are introduced. Presentation of agent technology and the use of ontologies are added. A new machine-learning chapter is based on stochastic methods, including first-order Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation. A new presentation of parameter fitting with expectation maximization learning and structure learning using Markov chain Monte Carlo sampling. Use of Markov decision processes in reinforcement learning. Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi, are added. A new supplemental programming book is available online and in print: "AI Algorithms in Prolog, Lisp and Java (TM). "References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence."@en
  • "This edition of 'Artificial Intelligence' includes increased coverage of the stochastic approaches to AI and stochastic methodology. Various sections have also been extended to recognize the importance of agent-based problem solving and embodiment in AI technology."
  • "Much has changed since the early editions of Artificial Intelligence were published. To reflect this the introductory material of this fifth edition has been substantially revised and rewritten to capture the excitement of the latest developments in AI work. Artificial intelligence is a diverse field. To ask the question "what is intelligence?" is to invite as many answers as there are approaches to the subject of artificial intelligence. These could be intelligent agents, logical reasoning, neural networks, expert systems, evolutionary computing and so on. This fifth edition covers all the main strategies used for creating computer systems that will behave in "intelligent" ways. It combines the broadest approach of any text in the marketplace with the practical information necessary to implement the strategies discussed, showing how to do this through Prolog or LISP programming."

http://schema.org/genre

  • "Electronic books"
  • "Electronic books"@en
  • "Matériel didactique"
  • "Lehrbuch"
  • "Lehrbuch"@en

http://schema.org/name

  • "Artificial Intelligence : Structures and Strategies for Complex Problem Solving"
  • "Artificial Intelligence: Structures and Strategies for Complex Problem Solving"
  • "Artifical intelligence : structures and strategies for complex problem solving"@en
  • "Ren gong zhi neng : fu za wen ti qiu jie de jie gou he ce lüe"
  • "Künstliche Intelligenz : Strategien zur Lösung komplexer Probleme"
  • "Artificial intelligence structures and strategies for complex problem solving"@en
  • "Artificial intelligence structures and strategies for complex problem solving"
  • "Artificial Intelligence : structures and Strategies for Complex Problem Solving"
  • "Künstliche Intelligenz"
  • "Artificial intelligence : structures and strategies for complex problem solving"
  • "Artificial intelligence : structures and strategies for complex problem solving"@en
  • "Artificial intelligence"
  • "Artificial intelligence : Structures and strategies for complex problem solving"
  • "人工智能 : 复杂问题求解的结构和策略"

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