000			02856nam a2200241Ia 4500
003			NULRC
005			20250520100647.0
008			250520s9999 xx 000 0 und d
020			_a9789810695958
040			_cNULRC
050			_aQ 335 .L84 2010
100			_aLuger, George F. _eauthor
245		0	_aArtificial intelligence : _bstructures and strategies for complex problem solving / _cGeorge F. Luger
250			_aSixth edition. | Lower Price edition/Philippine edition.
260			_aBoston, Massachusetts : _bPearson Addison-Wesley, _cc2010
300			_axxiii, 754 pages : _billustrations ; _c24 cm
365			_bPHP995
504			_aIncludes bibliographical references (pages 705-733) and index.
505			_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			_aARTIFICIAL INTELLIGENCE
942			_2lcc _cBK
999			_c10696 _d10696