View chapters 3 and 4 from the upcoming Third Edition. For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence. The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Click on "Features" tab below for more information Resources: Visit the author's website http://aima.cs.berkeley.edu/ to access both student and instructor resources including Power Point slides, syllabus. homework and exams, and solutions text problems.
Table of Contents
I. ARTIFICIAL INTELLIGENCE.
2. Intelligent Agents.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
12. Planning and Acting in the Read World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
18. Learning from Observations.
19. Statistical Learning.
20. Reinforcement Learning.
21. Knowledge in Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
26. Philosophical Foundations.
27. AI: Present and Future.
Artificial Intelligence(AI) is a big field, and this is a big book. We have tried to explore the full breadth of the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning, learning, and action; and everything from microelectronic devices to robotic planetary explorers. The book is also big because we go into some depth in presenting results, although we strive to cover only the most central ideas in the main part of each chapter. Pointers are given to further results in the bibliographical notes at the end of each chapter. The subtitle of this book is "A Modern Approach:" The intended meaning of this rather empty phrase is that we have tried to synthesize what is now known into a common framework, rather than trying to explain each subfield of AI in its own historical context. We apologize to those whose subfields are, as a result, less recognizable than they might otherwise have been. The main unifying theme is the idea of anintelligent agent.We define AI as the study of agents that receive percepts from the environment and perform actions. Each such agent implements a function that maps percept sequences to actions, and we cover different ways to represent these functions, such as production systems, reactive agents, real-time conditional planners, neural networks, and decision-theoretic systems. We explain the role of learning as extending the reach of the designer into unknown environments, and we show how that role constrains agent design, favoring explicit knowledge representation and reasoning. We treat robotics and vision not as independently defined problems, but as occurring in the service of achieving goals. We stress the importance of the task environment in determining the appropriate agent design. Our primary aim is to convey theideasthat have emerged over the past fifty years of AI research and the past two millenia of related work. We have tried to avoid excessive formality in the presentation of these ideas while retaining precision. Wherever appropriate, we have included pseudocode algorithms to make the ideas concrete; our pseudocode is described briefly in Appendix B. Implementations in several programming languages are available on the book's Web site,aima.cs.berkeley.edu. This book is primarily intended for use in an undergraduate course or course sequence. It can also be used in a graduate-level course (perhaps with the addition of some of the primary sources suggested in the bibliographical notes). Because of its comprehensive coverage and large number of detailed algorithms, it is useful as a primary reference volume for AI graduate students and professionals wishing to branch out beyond their own subfield. The only prerequisite is familiarity with basic concepts of computer science (algorithms, data structures, complexity) at a sophomore level. Freshman calculus is useful for understanding neural networks and statistical learning in detail. Some of the required mathematical background is supplied in Appendix A. Overview of the book The book is divided into eight parts. Part I,Artificial Intelligence,offers a view of the AI enterprise based around the idea of intelligent agents--systems that can decide what to do and then do it. Part II,Problem Solving,concentrates on methods for deciding what to do when one needs to think ahead several steps--for example in navigating across a country or playing chess. Part III,Knowledge and Reasoning,discusses ways to represent knowledge about the world--how it works, what it is currently like, and what one's actions might do--and how to reason logically with that knowledge. Part IV,Planning,then discusses how to use these reasoning methods to decide what to do, particularly by constructingplans.Part V,Uncertain Knowledge and Reasoning,is analogous to Parts III and IV, but it concentrat