Artificial Intelligence: Structures and Strategies for Complex Problem Solving, 6/e (Hardcover)
暫譯: 人工智慧:複雜問題解決的結構與策略,第6版(精裝本)
George F. Luger
- 出版商: Addison Wesley
- 出版日期: 2008-02-26
- 售價: $8,300
- 貴賓價: 9.5 折 $7,885
- 語言: 英文
- 頁數: 792
- 裝訂: Hardcover
- ISBN: 0321545893
- ISBN-13: 9780321545893
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相關分類:
人工智慧
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商品描述
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 ™. References and citations are updated throughout the Sixth Edition. For all readers interested in artificial intelligence.
商品描述(中文翻譯)
在這本易於理解且內容全面的書籍中,喬治·盧格(George Luger)捕捉了人工智慧的本質——解決計算機技術應用中出現的複雜問題。介紹了包括邏輯、語義網絡、連結主義網絡、圖形模型等多種關鍵表示技術。增加了代理技術的介紹及本體論的使用。新的機器學習章節基於隨機方法,包括一階貝葉斯網絡、隱藏馬可夫模型的變體、使用馬可夫隨機場的推斷以及循環信念傳播。對參數擬合的全新介紹,使用期望最大化學習和馬可夫鏈蒙特卡羅取樣的結構學習。強化學習中使用馬可夫決策過程。新增了自然語言處理的內容,使用動態規劃(Earley 解析器)及其他概率解析技術,包括維特比(Viterbi)算法。一本新的補充編程書籍《AI Algorithms in Prolog, Lisp and Java ™》可在線和印刷版獲得。第六版中的參考文獻和引用已更新。適合所有對人工智慧感興趣的讀者。