Principles of Artificial Intelligence
Previous 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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PRODUCTION SYSTEMS AND AI
12 SPECIALIZED PRODUCTION SYSTEMS
13 COMMENTS ON THE DIFFERENT TYPES OF PRODUCTION SYSTEMS
14 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
SEARCH STRATEGIES FOR AI PRODUCTION SYSTEMS
21 BACKTRACKING STRATEGIES
22 GRAPHSEARCH STRATEGIES
66 CONTROL KNOWLEDGE FOR RULEBASED DEDUCTION SYSTEMS
67 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
BASIC PLANGENERATING SYSTEMS
72 A FORWARD PRODUCTION SYSTEM
73 A REPRESENTATION FOR PLANS
74 A BACKWARD PRODUCTION SYSTEM
23 UNINFORMED GRAPHSEARCH PROCEDURES
24 HEURISTIC GRAPHSEARCH PROCEDURES
25 RELATED ALGORITHMS
26 MEASURES OF PERFORMANCE
SEARCH STRATEGIES FOR DECOMPOSABLE PRODUCTION SYSTEMS
A HEURISTIC SEARCH PROCEDURE FOR ANDOR GRAPHS
DECOMPOSABLE AND COMMUTATIVE SYSTEMS
34 SEARCHING GAME TREES
35 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
THE PREDICATE CALCULUS IN AI
43 THE USE OF THE PREDICATE CALCULUS IN AI
44 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
RESOLUTION REFUTATION SYSTEMS
51 PRODUCTION SYSTEMS FOR RESOLUTION REFUTATIONS
52 CONTROL STRATEGIES FOR RESOLUTION METHODS
53 SIMPLIFICATION STRATEGIES
54 EXTRACTING ANSWERS FROM RESOLUTION REFUTATIONS
RULEBASED DEDUCTION SYSTEMS
61 A FORWARD DEDUCTION SYSTEM
62 A BACKWARD DEDUCTION SYSTEM
63 RESOLVING WITHIN ANDOR GRAPHS
64 COMPUTATION DEDUCTIONS AND PROGRAM SYNTHESIS
65 A COMBINATION FORWARD AND BACKWARD SYSTEM
76 USING DEDUCTION SYSTEMS TO GENERATE ROBOT PLANS
77 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
ADVANCED PLANGENERATING SYSTEMS
83 AMENDING PLANS
84 HIERARCHICAL PLANNING
85 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
STRUCTURED OBJECT REPRESENTATIONS
91 FROM PREDICATE CALCULUS TO UNITS
94 DEDUCTIVE OPERATIONS ON STRUCTURED OBJECTS
95 DEFAULTS AND CONTRADICTORY INFORMATION
96 BIBLIOGRAPHICAL AND HISTORICAL REMARKS
101 AI SYSTEM ARCHITECTURES
102 KNOWLEDGE ACQUISITION
103 REPRESENTATIONAL FORMALISMS
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achieve actions algorithm AND/OR graph answer applied arcs Artificial Intelligence assertions assume attempt backtracking backward block called chapter clause complete component condition consider consistent contains control strategy corresponding cost database deduction delete described direction discussed efficient evaluation example expression F-rule fact Figure formula function given global database goal goal node goal wff heuristic implication important initial involves JOHN knowledge labeled language literals logic match methods move namely node Note obtained occur operation path possible precondition predicate calculus problem procedure production system proof prove quantified reasoning refutation represent representation resolution result robot rule satisfied selected sequence shown in Figure simple solution graph solve specify stack statement step STRIPS structure subgoal substitutions successors Suppose symbols termination theorem unifying unit University variables
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...
Page 5 - It has led to several techniques for modeling states of the world and for describing the process of change from one world state to another. It has led to a better understanding of how to generate plans for action sequences and how to monitor the execution of these plans. Complex robot control problems have forced...
Page 5 - Robotics The problem of controlling the physical actions of a mobile robot might not seem to require much intelligence. Even small children are able to navigate successfully through their environment and to manipulate items, such as light switches, toy blocks, eating utensils, etc. However these same tasks, performed almost unconsciously by humans, when performed by a machine require many of the same abilities used in solving more intellectually demanding problems. Research on robots or robotics...
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.