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. |
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... find the search program such that the maximum number of searchers in use at any move is minimized . The minimum number of searchers needed to clear the graph is related to the parameter called pathwidth . Dendris et al . [ 7 ] studied a ...
... graphs is currently the most popular graph algorithmic search paradigm, with applications in recognition of restricted graph families, diameter approximation for restricted families and determining a dominating pair in an AT-free graph ...
... graph . This paper surveys this area and provides new directions for further re- search in the area of graph searching . 1 Introduction Graph searching is a fundamental paradigm that pervades graph algorithms . A search of a graph ...
... Search Quality Maxim Gurevich, Technion, Israel Objectively assessing search quality is of great interest both to end-users and to search providers. Quality parameters like ranking quality, coverage of the web, index freshness, topic ...
... graphs with a reduced set of nodes is subsequently used as template graph . This reduction diminishes the search effort significantly . Definition 12 ( P - SYMMETRIC PARTITION ) . Given a template graph T = ( VT , ST , ET , T ) VÃ ET ...
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 |