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039		9	_a201402040051 _bVLOAD _c201008091003 _dmalmash _c200811091315 _dvenkatrajand _c200810291425 _dNoora _y200810291422 _zNoora
050			_aQ335 _b.R86 2003
100	1		_aRussell, Stuart J. _q(Stuart Jonathan) _925075
245	1	0	_aArtificial Intelligence : _bA Modern Approach / _cStuart J. Russell and Peter Norvig ; contributing writers John F. Canny ... [et al.].
250			_a2nd ed.
260			_aUpper Saddle River, N.J. : _bPrentice Hall, _c2003.
300			_axxviii, 1081 p. : _bill. ; _c24 cm.
440		0	_aPrentice Hall series in artificial intelligence _925076
500			_aPrevious ed.: 1995.
504			_aIncludes bibliographical references and index.
505			_a. ARTIFICIAL INTELLIGENCE. 1. Introduction. 2. Intelligent Agents. II. PROBLEM-SOLVING. 3. Solving Problems by Searching. 4. Informed Search and Exploration. 5. Constraint Satisfaction Problems. 6. Adversarial Search. III. KNOWLEDGE AND REASONING. 7. Logical Agents. 8. First-Order Logic. 9. Inference in First-Order Logic. 10. Knowledge Representation. IV. PLANNING. 11. Planning. 12. Planning and Acting in the Read World. V. UNCERTAIN KNOWLEDGE AND REASONING. 13. Uncertainty. 14. Probabilistic Reasoning Systems. 15. Probabilistic Reasoning Over Time. 16. Making Simple Decisions. 17. Making Complex Decisions. VI. LEARNING. 18. Learning from Observations. 19. Knowledge in Learning. 20. Statistical Learning Methods. 21. Reinforcement Learning. VII. COMMUNICATING, PERCEIVING, AND ACTING. 22. Agents that Communicate. 23. Text Processing in the Large. 24. Perception. 25. Robotics. VIII. CONCLUSIONS. 26. Philosophical Foundations. 27. AI: Present and Future.
520			_aThe first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field. In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI. The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to AI on the Web, and an online discussion group. All of this is available at: aima.cs.berkeley.edu
650		0	_aArtificial intelligence. _95146
942			_2lcc _n0 _cBK
999			_c16775 _d16775