000			03615cam a22004334i 4500
999			_c79619 _d316896
001			183611012
003			OCoLC
005			20220213092943.0
008			071207s2021 maua b 001 0 eng
010			_a2007050376
020			_a9789354493782
020			_a0321545893 (alk. paper)
020			_a9780132090018 (pbk.)
020			_a0132090015 (pbk.)
035			_a(OCoLC)183611012
040			_aDLC _beng _erda _cDLC _dBAKER _dYDXCP _dC#P _dBTCTA _dU9S _dALAUL _dW2U _dUXS _dDEBBG
050	0	0	_aQ335 _b.L84 2009
082	0	0	_a006.3 _222
082	0	4	_a006.3 _bLGA _222
100	1		_aLuger, George F. _989088
245	1	0	_aArtificial intelligence : _bstructures and strategies for complex problem solving / _cGeorge F. Luger.
250			_aSixth edition.
264		1	_aBoston : _bPearson Addison-Wesley, _c2021
264		4	_c2021
300			_axxiii, 754 pages : _billustrations ; _c24 cm
504			_aIncludes bibliographical references (p. 705-733) and indexes.
505	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.
520			_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.
650		0	_aArtificial intelligence. _989089
650		0	_aKnowledge representation (Information theory) _989090
650		0	_aProblem solving. _989091
650		0	_aProlog (Computer program language) _989092
650		0	_aLISP (Computer program language) _97743
650		4	_aInteligencia artificial. _989093
650		4	_aSoluci©đn de problemas. _989094
650		4	_aC©đdigos de correcci©đn (Teor©Ưa de la informaci©đn). _989095
856	4	1	_3Table of contents _uhttp://www.loc.gov/catdir/toc/fy0803/2007050376.html
942			_2ddc _cBOOK