03615cam a22004334i 4500 79619 316896 183611012 OCoLC 20220213092943.0 071207s2021 maua b 001 0 eng 2007050376 9789354493782 0321545893 (alk. paper) 9780132090018 (pbk.) 0132090015 (pbk.) (OCoLC)183611012 DLC eng rda DLC BAKER YDXCP C#P BTCTA U9S ALAUL W2U UXS DEBBG Q335 .L84 2009 006.3 22 006.3 LGA 22 Luger, George F. 89088 Artificial intelligence : structures and strategies for complex problem solving / George F. Luger. Sixth edition. Boston : Pearson Addison-Wesley, 2021 2021 xxiii, 754 pages : illustrations ; 24 cm Includes bibliographical references (p. 705-733) and indexes. 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. 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. Artificial intelligence. 89089 Knowledge representation (Information theory) 89090 Problem solving. 89091 Prolog (Computer program language) 89092 LISP (Computer program language) 7743 Inteligencia artificial. 89093 Soluci©đn de problemas. 89094 C©đdigos de correcci©đn (Teor©Ưa de la informaci©đn). 89095 Table of contents http://www.loc.gov/catdir/toc/fy0803/2007050376.html ddc BOOK