Artificial Intelligence

468 Pages·1975·24.031 MB·English

by  Earl B. Hunt, Edward C. Carterette and Morton P. Friedman (Auth.)

ACADEMIC PRESS
SERIES IN COGNITION AND PERCEPTION
SERIES ED/TORS:
Edward C. Carterette
Morton P. Friedman
Department of Psychology
University of California, Los Angeles
Los Angeles, California
Stephen K. Reed: Psychological Processes in Pattern Recognition
Earl B. Hunt: Artificial Intelligence
IN PREPARATION
James P. Egan: Signal Detection Theory and ROC-Analysis
Artificial Intelligence
EARL B. HUNT
Department of Psychology
University of Washington
Seattle, Washington
ACADEMIC PRESS New York San Francisco London 1975
A Subsidiary of Harcourt Brace Jovanovich, Publishers
COPYRIGHT © 1975, BY ACADEMIC PRESS, INC.
ALL RIGHTS RESERVED.
NO PART OF THIS PUBLICATION MAY BE REPRODUCED OR
TRANSMITTED IN ANY FORM OR BY ANY MEANS, ELECTRONIC
OR MECHANICAL, INCLUDING PHOTOCOPY, RECORDING, OR ANY
INFORMATION STORAGE AND RETRIEVAL SYSTEM, WITHOUT
PERMISSION IN WRITING FROM THE PUBLISHER.
ACADEMIC PRESS, INC.
Ill Fifth Avenue, New York, New York 10003
United Kingdom Edition published by
ACADEMIC PRESS, INC. (LONDON) LTD.
24/28 Oval Road, London NW1
Library of Congress Cataloging in Publication Data
Hunt, Earl B
Artificial intelligence.
(Academic Press series in cognition and perception)
Bibliography: p.
1. Artificial intelligence. [DNLM: 1. Computers.
2. Cybernetics. Q335 H939a]
Q335.H79 1974 001.53'5 74-1626
ISBN 0-12-362340-5
PRINTED IN THE UNITED STATES OF AMERICA
PREFACE
For the artificial intelligence field, this is a very conservative book. My motiva-
tion for writing it developed after a number of observations of how people with one
background zestfully approach another area with very little awareness of what has
been done before. I also believe that training in the artificial intelligence field, and
to a lesser extent, in computer science in general, is too bound to fads. Too much
effort is placed on putting the student at the forefront ofthe field and too little in
defining and presenting basic information. In my university, for instance, the first
course in artificial intelligence (obviously not taught by me) deals with the interest-
ing but tangentially relevant theories of Piaget on human cognitive development,
and then plunges the first-year graduate students into detailed studies of the latest
(often good) doctoral theses from Massachusetts Institute of Technology. The
trouble with this approach, as I see it, is that the student is offered a good view of
a few trees (chosen by the professor), and no idea either of the forest or of botany.
In other words, I think that the professors and the texts should assume more
responsibility for analyzing the basic principles and problems. After this is done, it
is appropriate to have seminars dealing with the latest, hot-off-the-press research.
So what is to be done? I have tried to gather together, in one place, a reasonably
detailed discussion of the basic mathematical and computational approaches to
problems in the artificial intelligence field. Emphasis has been on principles, not
details. Also, I have tried to focus on the major unsolved conceptual problems,
especially in the fields of machine comprehension and perception. You will find no
descriptions of robots in this book. Neither will you find a machine playing chess.
You will find discussions which, if you will wade through them, will help you under-
stand the basic approaches of machine pattern recognition, robot perception,
deductive question answering, and computer game playing.
The level of discussion is appropriate for seniors and graduate students in any of
the computer related sciences, or in experimental psychology. You must be easily
familiar with algebra, and should be passingly familiar with the concepts of proba-
bility theory, calculus, and symbolic logic. Persons who do not have some mathe-
matical background, however, probably will not find this text very illuminating.
IX
ACKNOWLEDGMENTS
Dr. J. Ross Quinlan, of the University of Sydney and I had intended to write this
book together, and did work on one or two chapters. Alas, we were defeated by the
Pacific Ocean. It simply proved impossible to communicate frequently enough,
especially in the light of the rapidly expanding literature. Nevertheless, Ross is
responsible for a high proportion of those good ideas I have proposed and probably
should be blamed for very few of the bad ones . . . and I certainly don't know
which. Thanks, Ross.
My second thanks for scientific collaboration goes to Dr. Sharon Sickel, who
forced me to keep my nose to the grindstone of mathematical theorem proving,
just to be sure I knew what she was talking about. My discussion of theorem proving
owes a great deal to her clear and precise reasoning.
A number of other associates have helped in various ways, either by their assis-
tance in some of the specific research problems described here, or by their com-
ments on various proposals and on earlier drafts of the text. I would like to mention
particularly, Mark Stickel,David Garnatz, Patrick Russell, Edward Strasbourger, and
Michael Delay, all of the University of Washington. Very helpful comments on the
text have been received from Edward Carterette of U.C.L.A., Cordell Green of
Stanford University, and A. A. Marley, of McGill.
Most of the original research reported here was sponsored by the U.S. Air Force,
Office of Scientific Research (Air Systems Command). Indeed, the general plan of
the book arose from discussions I had with those charged with various Air Force
computer applications, when I asked what sort of consulting guidance they could
use. It was there that a need for a book of basic techniques was most apparent, and
most clearly stated. Dr. Glenn Finch, now retired, but then with the Office of
Scientific Research, was a staunch source of encouragement and support for
several years.
Many secretaries worked on this manuscript at various stages. I won't try to
name them. They all did a good job. Without the courtesy of the various scientists
who responded to preprint requests and put me on their mailing lists, I could not
have written a book of this nature, nor could anyone else.
During the period in which I wrote this book I held the post of Chairman of the
Psychology Department at the University of Washington. My colleagues in that
department respected the idea that their administrator should have a scholarly life
as well, and regulated their requests for services so that it was possible for me to
maintain a scientific career. I appreciate this.
xi
Xll ACKNOWLEDGMENTS
Neither Academic Press nor my family left me during long periods when I failed
to communicate and did not seem to have produced any tangible results. Complet-
ing this book provides Academic Press with a manuscript and my family with more
of my time. I hope both press and people will be pleased.
Chapter I
THE SCOPE OF ARTIFICIAL INTELLIGENCE
1.0 IS THERE SUCH A THING?
There are over 100 computer science curricula in universities in the United
States. Practically every one of them includes a course entitled artificial
intelligence. In fact, it appears that more people study programming techniques
developed for artificial intelligence research than study techniques for business-data
processing (Elliot, 1968). The educational programs are matched by comparable
research activity. There is a journal called Artificial Intelligence and a series of
scientific conference reports on machine intelligence. What gives rise to this
activity?
If you asked physicists or chemists to offer an abstract definition of their field,
you would expect to find them in substantial agreement. It is doubtful whether you
would find such agreement if you were to gather together the various scientists
studying artificial intelligence. Interestingly, however, you probably would find
agreement if you examined the details of the courses they teach and the research
they do. The Association for Computing Machinery's recommended computer
science curriculum contains an outline for course A9, Artificial Intelligence, which
lists theorem proving, game playing, pattern recognition, problem solving, adaptive
programming, decision making, music composition by computer, learning networks,
natural-language data processing, and verbal and concept learning as suitable topics
(ACM Curriculum Committee, 1968). Similar veins run through the research
literature. No one can question the existence of a scientific field of activity. But
what is its significance?
One of the tests that has been proposed to determine the viability of a scientific
field asks the question, "Is it having an effect on fields closely related to it?" By
this criterion, artificial intelligence rates rather well. Programming techniques
originally developed for artificial intelligence research have become widespread in
computer programming. Psychology has undeniably been influenced by the
concepts of artificial intelligence in a number of fields (Hunt, 1968; Frijida, 1972;
4 I THE SCOPE OF ARTIFICIAL INTELLIGENCE
Loehlin, 1968; Miller, Galanter, & Pribram, 1960; Weisstein, 1969). In chemistry,
artificial intelligence techniques have been applied to the analysis of data from mass
spectroscopes (Lederberg & Feigenbaum, 1968) and in planning the synthesis of
organic molecules (Corey & Wipke, 1969).
There appears to be a meaningful body of knowledge whose unifying principles
are difficult to identify. The problem seems to lie in the definition of intelligence.
Psychologists, who have faced the problem of defining intelligence for some time,
have adopted the pragmatic approach that intelligence is what the intelligence test
measures. I shall do the same. For the first 90% of this book, "artificial
intelligence" will simply be the collection of things taught in artificial intelligence
courses. I shall select a subset of these topics which appear to me to be most
interesting or important, and discuss them in some detail. By taking this approach, I
postpone such broad philosophical issues as, "Can a machine think?" and "Is it
possible to speak of machine understanding?" until a common background of
factual knowledge has been established. To do this, the book has been divided into
four sections. The first section contains, in this chapter, an overview of the various
fields of artificial intelligence and, in the next chapter, an attempt to connect
artificial intelligence problems to some of our notions of computability and
abstract computing devices. No attempt will be made to give a comprehensive
history, but a book of this nature should contain some overview and at least a few,
possibly idiosyncratic, notions of how the field developed historically. Similarly,
the chapter on abstract computing is not intended to be a rigorous introduction to
the field, but rather an overview of the general notion of computability, with
emphasis on the interaction between computability theory and artificial intelli-
gence.
The remaining chapters are divided into sections dealing with substantive
knowledge in three areas that are basic to all artificial intelligence: pattern
recognition, problem solving, and machine comprehension. No attempt has been
made to provide a comprehensive literature review. (There are well over 1500
relevant articles.) The goal has been to explain in detail those approaches and
principles which appear to have the greatest promise in each of the fields under
discussion. Completeness of coverage has thus been sacrificed; many artificial
intelligence projects are simply not mentioned.
When I began work on this book, it soon became apparent that an analysis of
previously published reports simply would not provide answers to several important
questions which could be raised. Therefore, in conjunction with the discussion,
previously unpublished research will be presented. For the most part, this work is
concentrated in the fields of theorem proving and pattern recognition, which are
the fields most crucial to other artificial intelligence endeavors. Most of the studies
can be characterized as attempts to fill gaps, rather than to extend our knowledge
of given approaches to a problem.
In the final chapter, I have confronted the philosophical questions which I had
been avoiding. My answer to the "Can a machine think?" will probably be an
unsatisfactory one to enthusiasts on both sides.
1.1 PROBLEM SOLVING 5
1.1 Problem Solving
"Problem solving" has a rather restricted meaning in the artificial intelligence
lexicon. A problem is said to exist when one is given a present state, a description
of the characteristics of a desired state, and the operations that can be used to go
from one state to another. There may be constraints specifying that certain states
are not to be visited at any point. This formulation is known as the state-space
approach. A few examples will show how generally applicable it is.
In the "missionaries and cannibals" problem, three missionaries and three
cannibals wish to cross a river from the left bank to the right. They have available a
boat which can take two people on a trip. All can row. The problem is to get all six
to the right bank, subject to the culinary constraint that at no time may the
number of missionaries on either side of the river be exceeded by the number of
cannibals on that side. To translate the puzzle into a formal problem, let a state be
defined by the number of missionaries and cannibals on the left bank and the
position of the boat. The starting position is (3, 3, L) and the goal state (0, 0, R).
The permissible moves of the boat define the operators.
In theorem proving, one is given a set of true statements (premises) and a
statement whose truth is not known (the theorem to be proved). By applying a
sequence of allowable operations, such as the inference rules of algebra or
trigonometry, expand the set of true statements to include the theorem. The states
are defined by the statements proven thus far; the inference rules define the
operators.
Given a chess position, change it into a position in which the opponent's king is
checkmated. En route to this position, avoid any position in which your own king
is checkmated or in which a stalemate occurs. The board positions define the states,
and the piece moves the operator.
It has been said that modern artificial intelligence research began with the efforts
of Allen Newell, Herbert Simon, and J. C. Shaw to write problem-solving programs
(Fiegenbaum, 1968). Their studies were conducted jointly at the RAND Corpora-
tion and the Carnegie Institute of Technology (now Carnegie-Mellon University).
Intellectually, the RAND-Carnegie work was important both in its own right and
because it set the tone for many other efforts. Technologically, the programming
methods developed in the course of the research have become widely used
throughout computer science.
Newell et al (1957) first produced the Logic Theorist (LT), a program designed
to prove theorems in the sentential calculus. The LT successfully proved 38 of the
52 theorems of Chapter 2 of Whitehead and Russell's Principia Mathematica.1 As
the Principia is regarded as one of the basic works establishing the logical
foundations of mathematics, LT's feats were bound to attract interest. It is easy
'Twelve of the 14 unsolved problems were not completed because of the physical
limitations of the computer then available. The others were beyond the capacity of the
algorithm for logical reasons. A modified LT operating on a larger computer later solved all 52
theorems (Stefferud, 1963).
6 I THE SCOPE OF ARTIFICIAL INTELLIGENCE
either to overstate or unduly minimize the significance of LT's success in
reproducing the Principles results (references are purposely omitted here), so one
wants to be aware of precisely what was done.
The proofs of the theorems in Chapter 2 of Whitehead and Russell would not be
considered deep by a mathematician. Bright university students can generally
produce most of them. The brilliance of Whitehead and Russell's accomplishment
was not in proving the theorems, but in realizing that their proof could be used as
the basis for a development which leads from logic to mathematics. This realization
was an act of creative insight which Newell, Shaw, and Simon made no attempt to
mimic on a machine. On the other hand, the problems of Chapter 2 are not exactly
trivial. These proofs probably are beyond the grasp of 60% of humanity, a fact one
is likely to overlook if his circle of acquaintances is restricted to present and future
Ph.D.'s. Newell et al. have repeatedly stated that the real significance of LT lies in
how it produced proofs, not in the proofs it produced. Theorem proving is an
example of a large class of problems for which there are solution techniques which
are known to work, but which are not practical to execute. For example, the
following exhaustive technique is guaranteed to produce a proof for any provable
theorem—but it may take a while. . . .
Beginning with the premises, write down all inferences that can be made by
combining two or more known true statements in various ways. Examine the set
of statements so produced, to see if it contains either the theorem or its negation.
In the former case, the theorem is proven; in the latter, it is disproven. If neither
case occurs, add the new set of statements to the premises and repeat the
procedure. There will be some number, n, such that a proof (or disproof) will be
produced on the nth step, but there is no guarantee what n will be.2
Newell, Shaw, and Simon called this procedure the "British Museum algorithm,"
since it seemed to them as sensible as placing monkeys in front of typewriters in
order to reproduce all the books in the British Museum. They suggested instead
following a heuristic approach, a term they took from Polya (1954, 1957), who
believed that most mathematical proofs are achieved by guessing the nature of a
solution, then proving that the guess is correct. Polya contrasted this with the
algorithmic technique of mechanically going through steps which are bound,
eventually, to result in the correct answer.3 The British Museum algorithm is clearly
algorithmic. What Newell and his colleagues set out to do was to write a set of rules
(i.e., a program) for generating guesses, then proving that they were correct. The
idea caught on quickly, and today "heuristic programming" is spoken of
2 The procedure may not terminate if the "theorem" to be proven is not derivable from the
axioms. This point is discussed in more detail in Part III.
3 Polya did not give a formal definition of either algorithm or heuristic, nor did Newell et al.
Currently within computer science the term algorithm is used to mean a procedure for
operating on strings of input sentences from a specified set of legal strings (Glushkov, 1967). By
this definition, any program is an algorithm, and the Newell et al. program should be thought of
as algorithms for generating guesses.