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...
| Main Authors: | , |
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| Format: | Book |
| Language: | English |
| Published: |
New York, N.Y. :
McGraw Hill LLC,
[2023]
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| Edition: | First edition |
| Series: | McGraw-Hill's AccessEngineeringLibrary
McGraw-Hill's AccessEngineeringLibrary |
| Subjects: |
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| 100 | 1 | |a Kubat, Miroslav, |d 1958- |e author |= ^A1609805 | |
| 100 | 1 | |a Kubát, Miroslav, |e author | |
| 245 | 1 | 0 | |a Fundamentals of Artificial Intelligence : |b Problem Solving and Automated Reasoning / |c Miroslav Kubat |
| 250 | |a First edition | ||
| 264 | 1 | |a New York, N.Y. : |b McGraw Hill LLC, |c [2023] | |
| 264 | 4 | |c ?2023 | |
| 264 | 4 | |c ©2023 | |
| 300 | |a 1 online resource (320 pages) : |b 70 illustrations | ||
| 300 | |a 1 online resource | ||
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| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a 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 | |
| 506 | |a Restricted for use by site license. | ||
| 520 | 0 | |a 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 | |
| 530 | |a Also available in print and PDF edition | ||
| 533 | |a Electronic reproduction |b New York, N.Y. : |c McGraw Hill, |d 2023. |n Mode of access: World Wide Web. |n System requirements: Web browser. |n Access may be restricted to users at subscribing institutions | ||
| 533 | |a Electronic reproduction |b New York, N.Y. : |c McGraw Hill, |d 2023. |n Mode of access: World Wide Web. |n System requirements: Web browser. |n Access may be restricted to users at subscribing institutions. | ||
| 538 | |a Mode of access: Internet via World Wide Web | ||
| 546 | |a In English | ||
| 588 | |a Description based on e-Publication PDF | ||
| 596 | |a 22 | ||
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| 650 | 0 | |a Artificial intelligence |v Textbooks | |
| 650 | 0 | |a Artificial intelligence |v Textbooks. | |
| 650 | 0 | |a Artificial intelligence | |
| 655 | 0 | |a Electronic books | |
| 776 | 0 | 8 | |i Print version: |t Fundamentals of Artificial Intelligence : Problem Solving and Automated Reasoning |b First edition. |d New York, N.Y. : McGraw-Hill Education, 2023 |z 9781260467789 |w (OCoLC)1341204170 |
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