Artificial intelligence : structures and strategies for complex problem solving / George F. Luger.

By: Material type: TextTextPublication details: Boston, MA : Pearson Education, 2009.Edition: 6thDescription: xxiii, 754 p; 24 cmContent type:
  • Text
Media type:
  • Unmediated
Carrier type:
  • Volume
ISBN:
  • 978-0-13-209001-8
Subject(s): LOC classification:
  • 006.3 L96
Contents:
pt. 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.
Summary: In 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.
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Item type Current library Call number Status Barcode
Books Books FEU - Cavite Higher Education HED Library 006.3 L96 (Browse shelf(Opens below)) Available HED2569

Includes bibliographic references and index

English

pt. 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.

In 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.

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