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BL
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| Record Number
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688665
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| Doc. No
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b510854
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| Main Entry
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Luger, George F.
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| Title & Author
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Artificial intelligence : : structures and strategies for complex problem solving /\ George F. Luger.
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| Edition Statement
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Sixth edition.
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| Publication Statement
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Boston :: Pearson Addison-Wesley,, [2009]
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, ©2009
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| Page. NO
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xxiii, 754 pages :: illustrations ;; 24 cm
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| ISBN
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0132090015 (pbk.)
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: 0321545893 (alk. paper)
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: 9780132090018 (pbk.)
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: 9780321545893 (alk. paper)
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| Bibliographies/Indexes
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Includes bibliographical references (p. 705-733) and indexes.
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| Contents
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Pt. I. Artificial intelligence : its roots and scope -- 1. AI : history and applications -- Pt. II. Artificial intelligence as representation and search -- 2. The predicate calculus -- 3. Structures and strategies for state space search -- 4. Heuristic search -- 5. Stochastic methods -- 6. Control and implementation of state space search -- Pt. III. Capturing intelligence : the AI challenge -- 7. Knowledge representation -- 8. Strong method problem solving -- 9. Reasoning in uncertain situations -- Pt. IV. Machine iearning -- 10. Machine learning : symbol-based -- 11. Machine learning : connectionist -- 12. Machine learning : genetic and emergent -- 13. Machine learning : probabilistic -- Pt. V. Advanced topics for AI problem solving -- 14. Automated reasoning -- 15. Understanding natural language -- Pt. VI. Epilogue -- 16. Artificial intelligence as empirical enquiry.
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| Abstract
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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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| Subject
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Artificial intelligence.
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| Subject
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Knowledge representation (Information theory)
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LISP (Computer program language)
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Problem solving.
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Prolog (Computer program language)
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| Subject
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Códigos de corrección (Teoría de la información).
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| Subject
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Inteligencia artificial.
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| Subject
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Solución de problemas.
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| Dewey Classification
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006.3
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006.3
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| LC Classification
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Q335.L84 2009
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