Artificial Intelligence Structures and Strategies for Complex Problem Solving

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  • Edition: 6th
  • Format: Paperback
  • Copyright: 2008-02-26
  • Publisher: Pearson

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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 trade;.References and citations are updated throughout the Sixth Edition.For all readers interested in artificial intelligence.

Author Biography

George Luger is currently a Professor of Computer Science, Linguistics, and Psychology at the University of New Mexico. He received his PhD from the University of Pennsylvania and spent five years researching and teaching at the Department of Artificial Intelligence at the University of Edinburgh.

Table of Contents

Artificial Intelligence: Its Roots and Scopep. 1
AI: History and Applicationsp. 3
From Eden to ENIAC: Attitudes toward Intelligence, Knowledge, and Human Artificep. 3
Overview of AI Application Areasp. 20
Artificial Intelligence A Summaryp. 30
Epilogue and Referencesp. 31
Exercisesp. 33
Artificial Intelligence as Representation and Searchp. 35
The Predicate Calculusp. 45
Introductionp. 45
The Propositional Calculusp. 45
The Predicate Calculusp. 50
Using Inference Rules to Produce Predicate Calculus Expressionsp. 62
Application: A Logic-Based Financial Advisorp. 73
Epilogue and Referencesp. 77
Exercisesp. 77
Structures and Strategies for State Space Searchp. 79
Introductionp. 79
Graph Theoryp. 82
Strategies for State Space Searchp. 93
Using the State Space to Represent Reasoning with the Predicate Calculusp. 107
Epilogue and Referencesp. 121
Exercisesp. 121
Heuristic Searchp. 123
Introductionp. 123
Hill Climbing and Dynamic Programmingp. 127
The Best-First Search Algorithmp. 133
Admissibility, Monotonicity, and Informednessp. 145
Using Heuristics in Gamesp. 150
Complexity Issuesp. 157
Epilogue and Referencesp. 161
Exercisesp. 162
stochastic methodsp. 165
Introductionp. 165
The Elements of Countingp. 167
Elements of Probability Theoryp. 170
Applications of the Stochastic Methodologyp. 182
Bayes Theoremp. 184
Epilogue and Referencesp. 190
Exercisesp. 191
Control and Implementation of State Space Searchp. 193
Introductionp. 193
Recursion-Based Searchp. 194
Production Systemsp. 200
The Blackboard Architecture for Problem Solvingp. 187
Epilogue and Referencesp. 219
Exercisesp. 220
Capturing Intelligence: The AI Challengep. 223
Knowledge Representationp. 227
Issues in Knowledge Representationp. 227
A Brief History of AI Representational Systemsp. 228
Conceptual Graphs: A Network Languagep. 248
Alternative Representations and Ontologiesp. 258
Agent Based and Distributed Problem Solvingp. 265
Epilogue and Referencesp. 270
Exercisesp. 273
Strong Method Problem Solvingp. 277
Introductionp. 277
Overview of Expert System Technologyp. 279
Rule-Based Expert Systemsp. 286
Model-Based, Case Based, and Hybrid Systemsp. 298
Planningp. 314
Epilogue and Referencesp. 329
Exercisesp. 331
Reasoning in Uncertain Situationsp. 333
Introductionp. 333
Logic-Based Abductive Inferencep. 335
Abduction: Alternatives to Logicp. 350
The Stochastic Approach to Uncertaintyp. 363
Epilogue and Referencesp. 378
Exercisesp. 380
Machine Learningp. 385
Machine Learning: Symbol-Basedp. 387
Introductionp. 387
A Framework for Symbol-based Learningp. 390
Version Space Searchp. 396
The ID3 Decision Tree Induction Algorithmp. 408
Inductive Bias and Learnabilityp. 417
Knowledge and Learningp. 422
Unsupervised Learningp. 433
Reinforcement Learningp. 442
Epilogue and Referencesp. 449
Exercisesp. 450
Machine Learning: Connectionistp. 453
Introductionp. 453
Foundations for Connectionist Networksp. 455
Perceptron Learningp. 458
Backpropagation Learningp. 467
Competitive Learningp. 474
Hebbian Coincidence Learningp. 484
Attractor Networks or Memoriesp. 495
Epilogue and Referencesp. 505
Exercises 506
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