Fundamentals of artificial intelligence : problem solving and automated reasoning / Miroslav Kubat.

Includes bibliographical references and index.

Cover
Title Page
Copyright Page
Dedication
Contents
Preface
Acknowledgment
1 Core AI: Problem Solving and Automated Reasoning
1.1 Early Milestones
1.2 Problem Solving
1.3 Automated Reasoning
1.4 Structure and Method
2 Blind Search
2.1 Motivation and Terminology
2.2 Depth-First and Breadth-First Search
2.3 Practical Considerations
2.4 Aspects of Search Performance
2.5 Iterative Deepening (and Broadening)
2.6 Practice Makes Perfect
2.7 Concluding Remarks
3 Heuristic Search and Annealing
3.1 Hill Climbing and Best-First Search
3.2 Practical Aspects of Evaluation Functions
3.3 A-Star and IDA-Star
3.4 Simulated Annealing
3.5 Role of Background Knowledge
3.6 Continuous Domains
3.7 Practice Makes Perfect
3.8 Concluding Remarks
4 Adversary Search
4.1 Typical Problems
4.2 Baseline Mini-Max
4.3 Heuristic Mini-Max
4.4 Alpha-Beta Pruning
4.5 Additional Game-Programming Techniques
4.6 Practice Makes Perfect
4.7 Concluding Remarks
5 Planning
5.1 Toy Blocks
5.2 Available Actions
5.3 Planning with STRIPS
5.4 Numeric Example
5.5 Advanced Applications of AI Planning
5.6 Practice Makes Perfect
5.7 Concluding Remarks
6 Genetic Algorithm
6.1 General Schema
6.2 Imperfect Copies and Survival
6.3 Alternative GA Operators
6.4 Potential Problems
6.5 Advanced Variations
6.6 GA and the Knapsack Problem
6.7 GA and the Prisoner?s Dilemma
6.8 Practice Makes Perfect
6.9 Concluding Remarks
7 Artificial Life
7.1 Emergent Properties
7.2 L-Systems
7.3 Cellular Automata
7.4 Conways? Game of Life
7.5 Practice Makes Perfect
7.6 Concluding Remarks
8 Emergent Properties and Swarm Intelligence
8.1 Ant-Colony Optimization
8.2 ACO Addressing the Traveling Salesman
8.3 Particle-Swarm Optimization
8.4 Artificial-Bees Colony, ABC
8.5 Practice Makes Perfect
8.6 Concluding Remarks
9 Elements of Automated Reasoning
9.1 Facts and Queries
9.2 Rules and Knowledge-Based Systems
9.3 Simple Reasoning with Rules
9.4 Practice Makes Perfect
9.5 Concluding Remarks
10 Logic and Reasoning, Simplified
10.1 Entailment, Inference, Theorem Proving
10.2 Reasoning with Modus Ponens
10.3 Reasoning Using the Resolution Principle
10.4 Expressing Knowledge in Normal Form
10.5 Practice Makes Perfect
10.6 Concluding Remarks
11 Logic and Reasoning Using Variables
11.1 Rules and Quantifiers
11.2 Removing Quantifiers
11.3 Binding, Unification, and Reasoning
11.4 Practical Inference Procedures
11.5 Practice Makes Perfect
11.6 Concluding Remarks
12 Alternative Ways of Representing Knowledge
12.1 Frames and Semantic Networks
12.2 Reasoning with Frame-Based Knowledge
12.3 N-ary Relations in Frames and SNs
12.4 Practice Makes Perfect
12.5 Concluding Remarks
13 Hurdles on the Road to Automated Reasoning
13.1 Tacit Assumptions
13.2 Non-Monotonicity
13.3 Mycin?s Uncertainty Factors
13.4 Practice Makes Perfect
13.5 Concluding Remarks
14 Probabilistic Reasoning
14.1 Theory of Probability (Revision)
14.2 Probability and Reasoning
14.3 Belief Networks
14.4 Dealing with More Realistic Domains
14.5 Demspter-Shafer Approach: Masses Instead of Probabilities
14.6 From Masses to Belief and Plausibility
14.7 DST Rule of Evidence Combination
14.8 Practice Makes Perfect
14.9 Concluding Remarks
15 Fuzzy Sets
15.1 Fuzziness of Real-World Concepts
15.2 Fuzzy Set Membership
15.3 Fuzziness versus Other Paradigms
15.4 Fuzzy Set Operations
15.5 Counting Linguistic Variables
15.6 Fuzzy Reasoning
15.7 Practice Makes Perfect
15.8 Concluding Remarks
16 Highs and Lows of Expert Systems
16.1 Early Pioneer: Mycin
16.2 Later Developments
16.3 Some Experience
16.4 Practice Makes Perfect
16.5 Concluding Remarks
17 Beyond Core AI
17.1 Computer Vision
17.2 Natural Language Processing
17.3 Machine Learning
17.4 Agent Technology
17.5 Concluding Remarks
18 Philosophical Musings
18.1 Turing Test
18.2 Chinese Room and Other Reservations
18.3 Engineer?s Perspective
18.4 Concluding Remarks
Bibliography
Index

"This comprehensive textbook focuses on the core techniques employed by today's artificial intelligence, including problem-solving by search techniques and swarm intelligence, and further knowledge representation, logic, automated reasoning, and uncertainty processing. Some information about planning techniques and expert systems is also provided. Fundamentals of Artificial Intelligence: Problem Solving and Automated Reasoning is written in a concise format, with a view to optimizing learning. Each chapter contains a brief historical overview and a Practice Makes Perfect section to encourage independent thought. The book includes many visuals that illustrate the essential ideas. Also, many easy-to-follow examples show how to use these ideas in practical implementations"-- Provided by publisher.