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
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... selection in irrevocable production systems is quite limited. Even though the control strategy cannot always select the best rule to apply at any stage, there are times where an irrevocable regime is appropriate. For example, if the ...
... selection. If no knowledge is available, rules can be selected according to some arbitrary scheme. Ultimately, control will backtrack to select the appropriate rule. Obviously, if good rule-selection knowledge can be used, backing up to ...
... selected, though, it need only select one applicable rule. Even though processing component databases in parallel is possible, we are typically interested in control strategies that process them in some serial order. There are two major ...
... selected according to their heuristic applicability. In Figure 1.13 we show an AND/OR tree that illustrates a possible search performed by a decomposable production system. The problem is to integrate 4 | —-ax (1 - x2)” Algebraic ...
... selection mechanism. Various AI programming languages use backtracking as a built-in search strategy [Bobrow and Raphael (1974)]. The literature on heuristic graph searching is extensive; several references are cited in the next two ...
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 |