Principles of Artificial Intelligence
A classic introduction to artificial intelligence intended to bridge the gap between theory and practice, Principles of Artificial Intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval. Rather than focusing on the subject matter of the applications, the book is organized around general computational concepts involving the kinds of data structures used, the types of operations performed on the data structures, and the properties of the control strategies used.
Principles of Artificial Intelligenceevolved from the author's courses and seminars at Stanford University and University of Massachusetts, Amherst, and is suitable for text use in a senior or graduate AI course, or for individual study.
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... Knowledge For Rule-Based Deduction Systems 257 6.7. Bibliographical and Historical Remarks 267 Exercises 270 CHAPTER 7: BASIC PLAN-GENERATING SYSTEMS 275 Robot Problem Solving 275 A Forward Production System 281 A Representation for ...
... System Architectures 418 10.2. Knowledge Acquisition 419 10.3. Representational Formalisms 422 BIBLIOGRAPHY 429 AUTHOR INDEX 467 SUBJECT INDEX 471 PREFACE Previous treatments of Artificial Intelligence (AI) divide the subject.
... knowledge of a list-processing language, such as LISP, would be helpful. A course organized around this book could comfortably occupy a full semester. If separate practical or theoretical material is added, the time required might be an ...
... knowledge to generate and understand highly condensed and streamlined messages: A word to the wise from the wise is sufficient. Thus generating and understanding language is an encoding and decoding problem of fantastic complexity. A ...
... knowledge and certain techniques for making inferences from that knowledge. Although we do not treat the language-processing problem as such in this book, we do describe some important methods for knowledge representation and processing ...
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
CHAPTER 4 THE PREDICATE CALCULUS IN AI
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS
CHAPTER 7 BASIC PLANGENERATING SYSTEMS
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS