000			03244nam a2200397Ia 4500
005			20260803094046.0
008			251212s2009 xx 000 0 und d
245	1		_aArtificial intelligence : _bstructures and strategies for complex problem solving / _cGeorge F. Luger.
100			_aLuger, George F.
260			_aBoston, MA : _bPearson Education, _c2009.
250			_a6th
300			_axxiii, 754 p.:
300			_c24 cm.
500			_aIncludes bibliographic references and index
020			_a978-0-13-209001-8
650			_aArtificial intelligence
650			_aKnowledge representation (Information theory)
650			_aLISP (Computer program language)
650			_aProlog (Computer program language)
650			_aProblem solving
650			_aArtificial intelligence _xMathematics
050			_a006.3 L96
943			_b0 _c0 _d0 _e1 _f0 _g3 _h0 _i0 _j0
944			_b1 _c1 _d0
953			_pHED2569
546			_aEnglish
953			_cCollLib
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.
591			_a1
336			_aText
337			_aUnmediated
338			_aVolume
942			_2lcc _cBK _arizal _b2015-04-07 23:18:18 _drizal _e2015-04-07 23:18:18
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