Luger George F. Artificial Intelligence. Structures and Strategies for Complex Problem Solving

6th edition. — Pearson, 2008. — 784 p. — ISBN10: 0321545893; ISBN13: 978-0321545893.This accessible, comprehensive book captures the essence of artificial intelligence -- solving the complex problems that arise wherever computer technology is applied. With his signature enthusiasm, George Luger demonstrates numerous techniques and strategies for addressing the many challenges facing computer scientists today. Diverse topics on this exciting and ever-evolving field range from perception and adaptation using neural networks and genetic algorithms, intelligent agents with ontologies, automated reasoning, natural language analysis, and stochastic approaches to machine learning.
This book is ideal for a one - or two-semester university course on AI.
New to 6th edition:
A new chapter on stochastic approaches to machine learning, including first-prder Bayesian networks, variants of hidden Markov models, inference with Markov random fields and loopy belief propagation.
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.
Presentation of agent technology and the use of ontologies.
Natural language processing with dynamic programming (the Earley parser) and other probabilistic parsing techniques including Viterbi.
A new supplemental programming book is available: AI Algorithms in Prolog, Lisp, and Java. Available online and in print, this book demonstrates these languages as tools for building many of the algorithms presented throughout Luger's AI book.Preface.
Publisher’s Acknowledgements.
Artificial Intelligence. Its Roots and Scope.
AI: history and applications.
Artificial intelligence as representation and search.
The predicate calculus.
Structures and strategies for state space search.
Heuristic search.
Stochastic methods.
Control and implementation of state space search.
Capturing Intelligence: The AI Challenge.
Knowledge representation.
Strong method problem solving.
Reasoning in uncertain situations.
Machine Learning.
Machine learning: symbol-based.
Machine learning: connectionist.
Machine learning: genetic and emergent.
Machine learning: probabilistic.
Advanced Topics for AI Problem Solving.
Automated reasoning.
Understanding natural language.
Epilogue.
Artificial intelligence as empirical enquiry.
Bibliography.
Author Index.
Subject Index.