Artificial Intelligence – A Modern Approach

Artificial Intelligence – A Modern Approach

 

There are many textbooks that offer an introduction to artificial intelligence (AI). This text has five principal features that together distinguish it from other texts. 1. Unified presentation of the field.

Some texts are organized from a historical perspective, describing each of the major problems and solutions that have been uncovered in 40 years of AI research. Although there is value to this perspective, the result is to give the impression of a dozen or so barely related subfields, each with its own techniques and problems. We have chosen to present AI as a unified field, working on a common problem in various guises. This has entailed some reinterpretation of past research, showing how it fits within a common framework and how it relates to other work that was historically separate. It has also led us to include material not normally covered in AI texts.

2. Intelligent agent design.

The unifying theme of the book is the concept of an intelligent agent. In this view, the problem of AI is to describe and build agents that receive percepts from the environment and perform actions. Each such agent is implemented by a function that maps percepts to actions, and we cover different ways to represent these functions, such as production systems, reactive agents, logical planners, neural networks, and decision-theoretic systems.

We explain the role of learning as extending the reach of the designer into unknown environments, and show how it 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 goal achievement. We stress the importance of the task environment characteristics in determining the appropriate agent design.

3. Comprehensive and up-to-date coverage.

We cover areas that are sometimes underemphasized, including reasoning under uncertainty, learning, neural networks, natural language, vision, robotics, and philosophical foundations. We cover many of the more recent ideas in the field, including simulated annealing, memory-bounded search, global ontologies, dynamic and adaptive probabilistic (Bayesian) networks, computational learning theory, and reinforcement learning. We also provide extensive notes and references on the historical sources and current literature for the main ideas in each chapter.

4. Equal emphasis on theory and practice.

Theory and practice are given equal emphasis. All material is grounded in first principles with rigorous theoretical analysis where appropriate, but the point of the theory is to get the concepts across and explain how they are used in actual, fielded systems. The reader of this book will come away with an appreciation for the basic concepts and mathematical methods of AI, and also with an idea of what can and cannot be done with today’s technology, at what cost, and using what techniques.

5. Understanding through implementation.

The principles of intelligent agent design are clarified by using them to actually build agents. Chapter 2 provides an overview of agent design, including a basic agent and environmen project. Subsequent chapters include programming exercises that ask the student to add >.

capabilities to the agent, making it behave more and more interestingly and (we hope) intelligently. Algorithms are presented at three levels of detail: prose descriptions and ! pseudo-code in the text, and complete Common Lisp programs available on the Internet or on floppy disk. All the agent programs are interoperable and work in a uniform framework for simulated environments.

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 the 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. We also hope that AI researchers could benefit from thinking about the unifying approach we advocate. 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 adaptive probabilistic networks in detail. Some experience with nonnumeric programming is desirable, but can be picked up in a few weeks study. We provide implementations of all algorithms in Common Lisp (see Appendix B), but other languages such as Scheme, Prolog, Smalltalk, C++, or ML could be used instead.

 

 

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