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Fundamentals of Artificial Intelligence : Problem Solving and Automated Reasoning /

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 pla...

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Bibliographic Details
Main Author: Kubat, Miroslav (Author)
Corporate Author: McGraw-Hill Companies
Format: eBook
Language:English
Language Notes:In English.
Published: New York, N.Y. : McGraw Hill LLC, [2023]
Edition:First edition.
Series:McGraw-Hill's AccessEngineeringLibrary.
Subjects:
Artificial intelligence > Textbooks.
Artificial intelligence.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • 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.