|Part I||Artificial Intelligence: Its Roots and Scope||1|
|1||AI: Early History and Applications||3|
|Part II||Artificial Intelligence as Representation and Search||35|
|2||The Predicate Calculus||45|
|3||Structures and Strategies for State Space Search||79|
|6||Building Control Algorithms for State Space Search||193|
|Part III||Representation and Intelligence: The AI Challenge||223|
|8||Strong Method Problem Solving||277|
|9||Reasoning in Uncertain Situations||333|
|Part IV||Machine Learning||385|
|10||Machine Learning: Symbol-Based||387|
|11||Machine Learning: Connectionist||453|
|12||Machine Learning: Social and Emergent||507|
|Part V||Advanced Topics for AI Problem Solving||545|
|14||Understanding Natural Language||591|
|Part VI||Languages and Programming Techniques for Artificial Intelligence||635|
|15||An Introduction to Prolog||641|
|16||An Introduction to Lisp||723|
|17||Artificial Intelligence as Empirical Enquiry||823|
Artificial Intelligence: Structures and Strategies for Complex Problem Solvingby George F. Luger
Pub. Date: 08/28/2008
Publisher: Pearson Education
The practical applications of AI have been kept within the context of its broader goal: understanding
Combines the theoretical foundations of intelligent problem-solving with he data structures and algorithms needed for its implementation. The book presents logic, rule, object and agent-based architectures, along with example programs written in LISP and PROLOG.
The practical applications of AI have been kept within the context of its broader goal: understanding the patterns of intelligence as it operates in this world of uncertainty, complexity and change.
The introductory and concluding chapters take a new look at the potentials and challenges facing artificial intelligence and cognitive science. An extended treatment of knowledge-based problem-solving is given including model-based and case-based reasoning.
Includes new material on:
Fundamentals of search, inference and knowledge representation
AI algorithms and data structures in LISP and PROLOG Production systems, blackboards, and meta-interpreters including planers, rule-based reasoners, and inheritance systems.
Machine-learning including ID3 with bagging and boosting, explanation based learning, PAC learning, and other forms of induction
Neural networks, including perceptrons, back propogation, Kohonen networks, Hopfield networks, Grossberg learning, and counterpropagation. Emergent and social methods of learning and adaptation, including genetic algorithms, genetic programming and artificial life.
Object and agent-based problem solving and other forms of advanced knowledge representation
- Pearson Education
- Publication date:
Table of Contents
Most Helpful Customer Reviews
See all customer reviews