Ideal For An Undergraduate Course In Artificial Intelligence, The Sixth Edition Presents The Fundamental Concepts Of The Discipline First And Then Goes Into Detail With The Practical Information Necessary To Implement The Algorithms And Strategies Discussed. It Includes Perception And Adaptation Using Neural Networks And Genetic Algorithms, Intelligent Agents With Ontologies, Automated Reasoning, Natural Language Analysis, And Stochastic Approaches To Machine Learning.

Salient Features

Chapter 13, Probabilistically-Based Machine Learning Covers Stochastic Methods That Support Machine Learning.

New! Natural Language Processing With Dynamic Programming (The Earley Parser) And Other Probabilistic Parsing Techniques Including Viterbi, Are Added To Chapter 15, Understanding Natural Language.

New! Presentation Of Agent Technology And The Use Of Ontologies Are Added To Chapter 7, Knowledge Presentation.

Expanded Stochastic Approaches To Reasoning In Uncertain Situations, Including Bayesian Belief Networks And Markov Models, Are Discussed In Chapter 9.