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Artificial Intelligence

Book Description

Recent decades have witnessed the emergence of artificial intelligence as a serious science and engineering discipline. This textbook, aimed at junior to senior undergraduate students and first-year graduate students, presents artificial intelligence (AI) using a coherent framework to study the design of intelligent computational agents. By showing how basic approaches fit into a multidimensional design space, readers can learn the fundamentals without losing sight of the bigger picture. The book balances theory and experiment, showing how to link them intimately together, and develops the science of AI together with its engineering applications. Although structured as a textbook, the book's straightforward, self-contained style will also appeal to a wide audience of professionals, researchers, and independent learners. AI is a rapidly developing field: this book encapsulates the latest results without being exhaustive and encyclopedic. The text is supported by an online learning environment, AIspace, http://aispace.org, so that students can experiment with the main AI algorithms plus problems, animations, lecture slides, and a knowledge representation system, AIlog, for experimentation and problem solving.

Note:The ebook version does not provide access to the companion files.

Table of Contents

  1. Coverpage
  2. Artificial Intelligence
  3. Title page
  4. Copyright page
  5. Dedication
  6. Contents
  7. Preface
  8. I Agents in the World: What Are Agents and How Can They Be Built?
    1. 1 Artificial Intelligence and Agents
      1. 1.1 What Is Artificial Intelligence?
      2. 1.2 A Brief History of AI
      3. 1.3 Agents Situated in Environments
      4. 1.4 Knowledge Representation
      5. 1.5 Dimensions of Complexity
      6. 1.6 Prototypical Applications
      7. 1.7 Overview of the Book
      8. 1.8 Review
      9. 1.9 References and Further Reading
      10. 1.10 Exercises
    2. 2 Agent Architectures and Hierarchical Control
      1. 2.1 Agents
      2. 2.2 Agent Systems
      3. 2.3 Hierarchical Control
      4. 2.4 Embedded and Simulated Agents
      5. 2.5 Acting with Reasoning
      6. 2.6 Review
      7. 2.7 References and Further Reading
      8. 2.8 Exercises
  9. II Representing and Reasoning
    1. 3 States and Searching
      1. 3.1 Problem Solving as Search
      2. 3.2 State Spaces
      3. 3.3 Graph Searching
      4. 3.4 A Generic Searching Algorithm
      5. 3.5 Uninformed Search Strategies
      6. 3.6 Heuristic Search
      7. 3.7 More Sophisticated Search
      8. 3.8 Review
      9. 3.9 References and Further Reading
      10. 3.10 Exercises
    2. 4 Features and Constraints
      1. 4.1 Features and States
      2. 4.2 PossibleWorlds, Variables, and Constraints
      3. 4.3 Generate-and-Test Algorithms
      4. 4.4 Solving CSPs Using Search
      5. 4.5 Consistency Algorithms
      6. 4.6 Domain Splitting
      7. 4.7 Variable Elimination
      8. 4.8 Local Search
      9. 4.9 Population-Based Methods
      10. 4.10 Optimization
      11. 4.11 Review
      12. 4.12 References and Further Reading
      13. 4.13 Exercises
    3. 5 Propositions and Inference
      1. 5.1 Propositions
      2. 5.2 Propositional Definite Clauses
      3. 5.3 Knowledge Representation Issues
      4. 5.4 Proving by Contradictions
      5. 5.5 Complete Knowledge Assumption
      6. 5.6 Abduction
      7. 5.7 Causal Models
      8. 5.8 Review
      9. 5.9 References and Further Reading
      10. 5.10 Exercises
    4. 6 Reasoning Under Uncertainty
      1. 6.1 Probability
      2. 6.2 Independence
      3. 6.3 Belief Networks
      4. 6.4 Probabilistic Inference
      5. 6.5 Probability and Time
      6. 6.6 Review
      7. 6.7 References and Further Reading
      8. 6.8 Exercises
  10. III Learning and Planning
    1. 7 Learning: Overview and Supervised Learning
      1. 7.1 Learning Issues
      2. 7.2 Supervised Learning
      3. 7.3 Basic Models for Supervised Learning
      4. 7.4 Composite Models
      5. 7.5 Avoiding Overfitting
      6. 7.6 Case-Based Reasoning
      7. 7.7 Learning as Refining the Hypothesis Space
      8. 7.8 Bayesian Learning
      9. 7.9 Review
      10. 7.10 References and Further Reading
      11. 7.11 Exercises
    2. 8 Planning with Certainty
      1. 8.1 Representing States, Actions, and Goals
      2. 8.2 Forward Planning
      3. 8.3 Regression Planning
      4. 8.4 Planning as a CSP
      5. 8.5 Partial-Order Planning
      6. 8.6 Review
      7. 8.7 References and Further Reading
      8. 8.8 Exercises
    3. 9 Planning Under Uncertainty
      1. 9.1 Preferences and Utility
      2. 9.2 One-Off Decisions
      3. 9.3 Sequential Decisions
      4. 9.4 The Value of Information and Control
      5. 9.5 Decision Processes
      6. 9.6 Review
      7. 9.7 References and Further Reading
      8. 9.8 Exercises
    4. 10 Multiagent Systems
      1. 10.1 Multiagent Framework
      2. 10.2 Representations of Games
      3. 10.3 Computing Strategies with Perfect Information
      4. 10.4 Partially Observable Multiagent Reasoning
      5. 10.5 Group Decision Making
      6. 10.6 Mechanism Design
      7. 10.7 Review
      8. 10.8 References and Further Reading
      9. 10.9 Exercises
    5. 11 Beyond Supervised Learning
      1. 11.1 Clustering
      2. 11.2 Learning Belief Networks
      3. 11.3 Reinforcement Learning
      4. 11.4 Review
      5. 11.5 References and Further Reading
      6. 11.6 Exercises
  11. IV Reasoning About Individuals and Relations
    1. 12 Individuals and Relations
      1. 12.1 Exploiting Structure Beyond Features
      2. 12.2 Symbols and Semantics
      3. 12.3 Datalog: A Relational Rule Language
      4. 12.4 Proofs and Substitutions
      5. 12.5 Function Symbols
      6. 12.6 Applications in Natural Language Processing
      7. 12.7 Equality
      8. 12.8 Complete Knowledge Assumption
      9. 12.9 Review
      10. 12.10 References and Further Reading
      11. 12.11 Exercises
    2. 13 Ontologies and Knowledge-Based Systems
      1. 13.1 Knowledge Sharing
      2. 13.2 Flexible Representations
      3. 13.3 Ontologies and Knowledge Sharing
      4. 13.4 Querying Users and Other Knowledge Sources
      5. 13.5 Implementing Knowledge-Based Systems
      6. 13.6 Review
      7. 13.7 References and Further Reading
      8. 13.8 Exercises
    3. 14 Relational Planning, Learning, and Probabilistic Reasoning
      1. 14.1 Planning with Individuals and Relations
      2. 14.2 Learning with Individuals and Relations
      3. 14.3 Probabilistic Relational Models
      4. 14.4 Review
      5. 14.5 References and Further Reading
      6. 14.6 Exercises
  12. V The Big Picture
    1. 15 Retrospect and Prospect
      1. 15.1 Dimensions of Complexity Revisited
      2. 15.2 Social and Ethical Consequences
      3. 15.3 References and Further Reading
  13. A Mathematical Preliminaries and Notation
    1. A.1 Discrete Mathematics
    2. A.2 Functions, Factors, and Arrays
    3. A.3 Relations and the Relational Algebra
  14. Bibliography
  15. Index