03616cam a22004334i 4500999001800000001001000018003000600028005001700034008004000051010001500091020001800106020002800124020002500152020002200177035002100199040007800220050002000298082001400318082001900332100002800351245010500379250001900484264004400503264000900547300004700556504006600603505088600669520118001555650003602735650005702771650002802828650004602856650004302902650003602945650003702981650007403018856007603092942001403168  c79619d316896183611012OCoLC20220213092943.0071207s2021   maua     b    001 0 eng    a2007050376  a9789354493782  a0321545893 (alk. paper)  a9780132090018 (pbk.)  a0132090015 (pbk.)  a(OCoLC)183611012  aDLCbengerdacDLCdBAKERdYDXCPdC#PdBTCTAdU9SdALAULdW2UdUXSdDEBBG00aQ335b.L84 200900a006.322204a006.3bLGA2221 aLuger, George F.98908810aArtificial intelligence :bstructures and strategies for complex problem solving /cGeorge F. Luger.  aSixth edition. 1aBoston :bPearson Addison-Wesley,c2021 4c2021  axxiii, 754 pages :billustrations ;c24 cm  aIncludes bibliographical references (p. 705-733) and indexes.0 aPt. 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.  aIn 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. 0aArtificial intelligence.989089 0aKnowledge representation (Information theory)989090 0aProblem solving.989091 0aProlog (Computer program language)989092 0aLISP (Computer program language)97743 4aInteligencia artificial.989093 4aSoluci©đn de problemas.989094 4aC©đdigos de correcci©đn (Teor©Ưa de la informaci©đn).989095413Table of contentsuhttp://www.loc.gov/catdir/toc/fy0803/2007050376.html  2ddccBOOK