This is an introduction to the theory and practice of artificial intelligence. It uses an intelligent agent as the unifying theme throughout, and covers areas that are sometimes underemphasized elsewhere. These include reasoning under uncertainty, learning, natural language, vision and robotics. The book also explains in detail some of the more recent ideas in the field, including simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural nets, inductive logic programming, computational learning theory, and reinforcement learning.

Table of Contents

I. ARTIFICIAL INTELLIGENCE. 1. Introduction. 2. Intelligent Agents. II. PROBLEM-SOLVING. 3. Solving Problems by Searching. 4. Informed Search Methods. 5. Game Playing. III. KNOWLEDGE AND REASONING. 6. Agents that Reason Logically. 7. First-order Logic. 8. Building a Knowledge Base. 9. Inference in First-Order Logic. 10. Logical Reasoning Systems. IV. ACTING LOGICALLY. 11. Planning. 12. Practical Planning. 13. Planning and Acting. V. UNCERTAIN KNOWLEDGE AND REASONING. 14. Uncertainty. 15. Probabilistic Reasoning Systems. 16. Making Simple Decisions. 17. Making Complex Decisions. VI. LEARNING. 18. Learning from Observations. 19. Learning with Neural Networks. 20. Reinforcement Learning. 21. Knowledge in Learning. VII. COMMUNICATING, PERCEIVING, AND ACTING. 22. Agents that Communicate. 23. Practical Communication in English. 24. Perception. 25. Robotics. VIII. CONCLUSIONS. 26. Philosophical Foundations. 27. AI: Present and Future.