Principles of Artificial IntelligencePrevious treatments of Artificial Intelligence (AI) divide the subject into its major areas of application, namely, natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, intelligent data retrieval systems, etc. The major difficulty with this approach is that these application areas are now so extensive, that each could, at best, be only superficially treated in a book of this length. Instead, I have attempted here to describe fundamental AI ideas that underlie many of these applications. My organization of these ideas is not, then, based on the subject matter of their application, but is, instead, based on general computational concepts involving the kinds of data structures used, the types of operations performed on these data struc tures, and the properties of con'trol strategies used by AI systems. I stress, in particular, the important roles played in AI by generalized production systems and the predicate calculus. The notes on which the book is based evolved in courses and seminars at Stanford University and at the University of Massachusetts at Amherst. Although certain topics treated in my previous book, Problem solving Methods in Artificial Intelligence, are covered here as well, this book contains many additional topics such as rule-based systems, robot problem-solving systems, and structured-object representations. |
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Contents
PRODUCTION SYSTEMS AND AI | 17 |
12 SPECIALIZED PRODUCTION SYSTEMS | 35 |
13 COMMENTS ON THE DIFFERENT TYPES OF PRODUCTION SYSTEMS | 47 |
14 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 48 |
EXERCISES | 50 |
SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS | 53 |
21 BACKTRACKING STRATEGIES | 55 |
22 GRAPHSEARCH STRATEGIES | 61 |
66 CONTROL KNOWLEDGE FOR RULEBASED DEDUCTION SYSTEMS | 257 |
67 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 267 |
EXERCISES | 270 |
BASIC PLANGENERATING SYSTEMS | 275 |
72 A FORWARD PRODUCTION SYSTEM | 281 |
73 A REPRESENTATION FOR PLANS | 282 |
74 A BACKWARD PRODUCTION SYSTEM | 287 |
75 STRIPS | 298 |
23 UNINFORMED GRAPHSEARCH PROCEDURES | 68 |
24 HEURISTIC GRAPHSEARCH PROCEDURES | 72 |
25 RELATED ALGORITHMS | 88 |
26 MEASURES OF PERFORMANCE | 91 |
EXERCISES | 96 |
SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS | 99 |
A HEURISTIC SEARCH PROCEDURE FOR ANDOR GRAPHS | 103 |
DECOMPOSABLE AND COMMUTATIVE SYSTEMS | 109 |
34 SEARCHING GAME TREES | 112 |
35 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 127 |
THE PREDICATE CALCULUS IN AI | 131 |
42 RESOLUTION | 145 |
43 THE USE OF THE PREDICATE CALCULUS IN AI | 152 |
44 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 156 |
RESOLUTION REFUTATION SYSTEMS | 161 |
51 PRODUCTION SYSTEMS FOR RESOLUTION REFUTATIONS | 163 |
52 CONTROL STRATEGIES FOR RESOLUTION METHODS | 164 |
53 SIMPLIFICATION STRATEGIES | 172 |
54 EXTRACTING ANSWERS FROM RESOLUTION REFUTATIONS | 175 |
189 | |
RULEBASED DEDUCTION SYSTEMS | 193 |
61 A FORWARD DEDUCTION SYSTEM | 196 |
62 A BACKWARD DEDUCTION SYSTEM | 212 |
63 RESOLVING WITHIN ANDOR GRAPHS | 234 |
64 COMPUTATION DEDUCTIONS AND PROGRAM SYNTHESIS | 241 |
65 A COMBINATION FORWARD AND BACKWARD SYSTEM | 253 |
76 USING DEDUCTION SYSTEMS TO GENERATE ROBOT PLANS | 307 |
77 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 315 |
EXERCISES | 317 |
ADVANCED PLANGENERATING SYSTEMS | 321 |
82 DCOMP | 333 |
83 AMENDING PLANS | 342 |
84 HIERARCHICAL PLANNING | 349 |
85 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 357 |
EXERCISES | 358 |
STRUCTURED OBJECT REPRESENTATIONS | 361 |
91 FROM PREDICATE CALCULUS TO UNITS | 362 |
SEMANTIC NETWORKS | 370 |
93 MATCHING | 378 |
94 DEDUCTIVE OPERATIONS ON STRUCTURED OBJECTS | 387 |
95 DEFAULTS AND CONTRADICTORY INFORMATION | 408 |
96 BIBLIOGRAPHICAL AND HISTORICAL REMARKS | 412 |
EXERCISES | 414 |
PROSPECTUS | 417 |
101 AI SYSTEM ARCHITECTURES | 418 |
102 KNOWLEDGE ACQUISITION | 419 |
103 REPRESENTATIONAL FORMALISMS | 422 |
BIBLIOGRAPHY | 429 |
467 | |
471 | |
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Common terms and phrases
8-puzzle achieve actions Adders AI production algorithm AND/OR graph applied Artificial Intelligence atomic formula backed-up value backtracking backward block breadth-first breadth-first search called chapter clause form CLEAR(C component control regime control strategy cost DCOMP Deleters delineation depth-first search described discussed disjunction domain element-of evaluation function example existentially quantified F-rule formula frame problem game tree global database goal expression goal node goal stack goal wff graph-search HANDEMPTY heuristic HOLDING(A implication initial state description knowledge literal nodes logic negation node labeled ONTABLE(A optimal path pickup(A precondition predicate calculus problem-solving procedure production system proof prove recursive regress represent representation resolution refutation result robot problem rule applications rule-based deduction systems search graph search tree selected semantic network sequence shown in Figure Skolem function solution graph solve stack(A STRIPS structure subgoal substitutions successors Suppose symbols termination condition theorem theorem-proving tip nodes universally quantified unstack(C,A variables WORKS-IN
Popular passages
Page 6 - One of the important contributions of research in automatic programming has been the notion of debugging as a problem-solving strategy. It has been found that it is often much more efficient to produce an...
Page 7 - These efforts were directed at making the time-versus-problem-size curve grow as slowly as possible, even when it must grow exponentially. Several methods have been developed for delaying and moderating the inevitable combinatorial explosion. Again, knowledge about the problem domain is the key to more efficient solution methods. Many of the methods developed to deal with combinatorial problems are also useful on other, less combinatorially severe problems.
Page 2 - ... It has been very difficult to develop computer systems capable of generating and "understanding" even fragments of a natural language, such as English. One source of the difficulty is that language has evolved as a communication medium between intelligent beings. Its primary use is for transmitting a bit of "mental structure...
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Page 4 - Many expert consulting systems employ the AI technique of rule-based deduction. In such systems, expert knowledge is represented as a large set of simple rules, and these rules are used to guide the dialogue between the system and the user and to deduce conclusions.