Principles of Artificial IntelligenceA 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. |
From inside the book
Results 1-5 of 49
... described as a “supercompiler,” or a program that could take in a very high-level description of what the program is to accomplish and produce a program. The high-level description might be a precise statement in a formal language, such ...
... described. Systems for proving theorems using resolution are discussed in chapter 5. We indicate how several different kinds of problems can be posed as theorem-proving problems. Chapter 6 examines some of the inadequacies of simple ...
... described in a book by Winograd (1972). The book by Newell et al. (1973) describes the five-year goals of a research project to develop a speech understanding system; the major results of this research are described in papers by Medress ...
... described by Duda et al. (1978a, 1978b, 1979). Several expert systems developed at Stanford University are summarized by Feigenbaum (1977). The most highly developed of these, DENDRAL, computes structural descriptions of complex organic ...
... described in several papers and reports. Good accounts are available for the MIT work by Winston (1972); for the Stanford Research Institute work by Raphael et al. (1971) and Raphael (1976, chapter 8); for the Stanford University work ...
Contents
1 | |
17 | |
53 | |
CHAPTER 3 SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |
CHAPTER 4 THE PREDICATE CALCULUS IN AI | 131 |
CHAPTER 5 RESOLUTION REFUTATION SYSTEMS | 161 |
CHAPTER 6 RULEBASED DEDUCTION SYSTEMS | 193 |
CHAPTER 7 BASIC PLANGENERATING SYSTEMS | 275 |
CHAPTER 8 ADVANCED PLANGENERATING SYSTEMS | 321 |
CHAPTER 9 STRUCTURED OBJECT REPRESENTATIONS | 361 |
PROSPECTUS | 417 |
BIBLIOGRAPHY | 429 |
AUTHOR INDEX | 467 |
SUBJECT INDEX | 471 |