Connectionistic Problem Solving: Computational Aspects of Biological Learning

276 Pages·1990·8.069 MB·English

by  Steven E. Hampson (auth.)

Connectionistic Problem Solving
Steven E. Hampson
Connectionistic Problem Solving
Computational Aspects of
Biological Learning
With 36 Figures
Birkhauser
Boston· Basel . Berlin
Steven E. Hampson
University of California
Department of Information
& Computer Science
Irvine, California 92717, USA
Library of Congress Cataloging-in-Publication Data
Hampson, Steven E.
Connectionistic problem solving : computational aspects of
biological learning / Steven E. Hampson.
p. cm.
Includes bibliographical references.
ISBN 0-8176-3450-9 (alk. paper)
I. Neural computers. 2. Artificial intelligence. I. Title.
QA76.5.H35413 1989
006.3--dc20 89-17921
Printed on acid-free paper.
© Birkhiiuser Boston, 1990
Softcover reprint of the hardcover 1s t edition 1990
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ISBN-13:978-0-8176-3450-6 e-ISBN-13:978-1-4684-6770-3
DOl: 10.1 007/978-1-4684-6770-3
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Acknowledgements
First and foremost, I would like to acknowledge the indispensable
support of my parents, Ross and Luella Hampson. They have been
unfiaggingly supportive (in every sense of the word) and deserve a good
deal of credit for their encouragement in pursuing my interests, over
sometimes more pragmatic courses of action.
I would also like to thank Joy and Steve Bench, Jack Beusmans,
Luella Hampson, John Justeson, Don McAfee and Dennis Volper who
read and commented on (and in the case of Dennis probably wrote)
various parts of this book at one time or another. All mathematical
analysis was done in collaboration with Dennis V olper. Thanks are also
due to Jim Hester for his help when "C" or UNIX™ were being
particularly obtuse.
Further back, thanks are still due to Bob Loomis for an ideal
introduction to good research. I can't claim to have matched his
example, but it's been something to shoot for. He resisted any attempts
to ascribe cognitive behavior to plants, but the mental processes (e.g.,
purposes, goals, intents, desires) of more advanced organisms (gastropods,
for example) are hopefully unassailable.
Finally, I would like to thank the numerous people at UCI who have
made life here pleasant, informative and productive.
Contents
Acknowledgements ................................................................... v
1 Introduction ...................................................................... 1
1.1 The problem and the approach ...................................................... 1
1.2 Adaptive problem solving .............................................................. 3
1.3 Starting with simple models........................................................... 5
1.4 Sequential vs. parallel processing ....................................... ............ 9
1.5 Learning ......................................................................................... 12
1.6 Overview of the model................................................................... 13
2 Node Structure and Training ........................................... 17
2.1 Introduction ................................................................................... 17
2.2 Node structure ............................................................................... 17
2.3 Node training ................................................................................. 23
2.4 Input order ..................................................................................... 29
2.5 Alternative LT U models ................................................................ 31
2.6 Continuous or multivalued valued features.................................... 34
2.7 Representing multivalued features ................................................. 36
2.8 Excitation, inhibition and facilitation ............................................ 37
3 Improving on Perceptron Training.................................. 41
3.1 Introduction .................................................................................... 41
3.2 Perceptron training time complexity.. ........................................... 41
3.3 Output-specific feature associability .............................................. 44
3.4 Origin placement............................................................................ 47
3.5 Short-term weight modification.... .................................................. 49
4 Learning and Using Specific Instances............................. 53
viii Contents
4.1 Introduction ...................................................................... 53
4.2 Focusing ............................................................................ 54
4.3 Generalization vs. specific instance learning .......... ............ 57
4.4 Use of specific instances..................................................... 60
5 Operator and Network Structure .................................... 71
5.1 Introduction ...................................................................... 71
5.2 Network interconnections and association types ................ 72
5.3 Multilayer networks........................................................ ... 75
5.4 Recurrent connections ....................................................... 77
5.5 Forms of category representation....................................... 81
5.6 Space requirements............................................................ 86
5.7 Goals as undistinguished features ...................................... 89
6 Operator Training............................................................. 97
6.1 Introduction ...................................................................... 97
6.2 Operator training (disjunctive representation) ................... 98
6.3 OT extensions ................................................................... 101
6.4 OT results ......................................................................... 103
6.5 Shared memory focusing ............................. ....................... 104
6.6 Behavioral results .............................................................. 109
6.7 Input-specific feature associ ability ..................................... 110
7 Learned Evaluation and Sequential Credit Assignment. 115
7.1 Introduction ...................................................................... 115
7.2 Single action ...................................................................... 116
7.3 Sequential action ............................................................... 118
7.4 Biological evaluation .......... ............... ..................... ........... 122
7.5 The S-R model .................................................................. 125
7.6 Variations on the theme .................................................... 127
7.7 Instrumental and classical conditioning ............................. 130
7.8 Results...... ... ....... .............. ........... .... ........... .......... ............ 132
8 Stimulus-Stimulus Associations and Parallel Search. ...... 139
8.1 Introduction ...................................................................... 139
8.2 The S-S model................................................................... 139
8.3 Backward search................................................................ 141
8.4 Eco-world: an example domain ............................ .............. 150
8.5 Forward search .................................................................. 162
9 Stimulus-Stimulus Discussion ........................................... 167
9.1 Introduction .................................................................. .... 167
9.2 Biological relevance ......................................... .................. 167
9.3 A simple experiment .......................................................... 174
9.4 Drive and reward........... ................... ..................... ............ 175
9.5 Automatization of behavior .......................... ............ ......... 178
9.6 Parallel vs. sequential search ............. ........... ..................... 180
Connectionistic Problem Solving ix
10 Stimulus-Goal Associations ............................................ 183
10.1 Introduction ..................................................................... 183
10.2 The S-G model ................................................................ 184
10.3 S-G discussion........................................................... ....... 189
10.4 General goal setting ......................................................... 191
11 Summary and Conclusions ............................................. 193
12 Further Reading and Notes ............................................ 199
13 Bibliography .................................................................... 205
14 Symbols and Abbreviations ............................................ 259
15 Name Index ..................................................................... 261
16 Subject Index .................................................................. 273
1 Introduction
1.1 The problem and the approach
The model developed here, which is actually more a collection of com
ponents than a single monolithic structure, traces a path from relatively
low-level neural/connectionistic structures and processes to relatively
high-level animal/artificial intelligence behaviors. Incremental extension
of this initial path permits increasingly sophisticated representation and
processing strategies, and consequently increasingly sophisticated
behavior. The initial chapters develop the basic components of the sys
tem at the node and network level, with the general goal of efficient
category learning and representation. The later chapters are more con
cerned with the problems of assembling sequences of actions in order to
achieve a given goal state.
The model is referred to as connectionistic rather than neural, be
cause, while the basic components are neuron-like, there is only limited
commitment to physiological realism. Consequently the neuron-like ele
ments are referred to as "nodes" rather than "neurons". The model is
directed more at the behavioral level, and at that level, numerous con
cepts from animal learning theory are directly applicable to connectionis
tic modeling. An attempt to actually implement these behavioral
theories in a computer simulation can be quite informative, as most are
only partially specified, and the gaps may be apparent only when actual
ly building a functioning system. In addition, a computer implementa
tion provides an improved capability to explore the strengths and limita
tions of the different approaches as well as their various interactions.
"Thought experiments" are useful, but only for systems of very limited
complexity.
2 Introduction
Connectionism, like artificial intelligence (AI), has no necessary com
mitment to biological relevance, but it is generally assumed that a better
understanding of biological intelligence has something to offer to the
study of artificial intelligence. Conversely, the formalisms and analysis
developed in the study of abstract intelligence help make sense of the
overwhelming abundance of biological data. Consequently, in the
development of the model, an attempt is made to maintain contact with
both biological and formal aspects of the task. The use of neuron-like
elements as the basic building blocks strongly biases the structure and
development of the model in a biological direction, and simple problems
and possible solutions which can be addressed in connectionistic networks
are often found to be reflected in biological structures and processes.
At the implementation level, a limited discussion of neurophysiology
provides a biological context for the connectionistic implementation. In
turn, the simplified connectionistic model provides a unified, functioning
structure on which disparate aspects of physiology can be hung to pro
duce a functioning whole. Analysis of these simplified structures gives
some insight into the computational characteristics of the corresponding
neural systems.
At the behavioral level, animal learning theory and AI have tradition
ally addressed different aspects of intelligent behavior. AI has been pri
marily concerned with the high-level "symbolic" aspects of intelligence
while animal behaviorists are more inclined toward "numeric" models of
relatively low-level intelligence. However, AI has recently expanded to
ward more numeric representations, and "cognitive" models of animal
behavior have gained ground. Consequently, there is increasing overlap
of interest. The field of connectionism provides a good meeting place
since it is concerned with both high-level (e.g., language) and low-level
(e.g., vision or motor control) tasks, and both formal and biologically
motivated models are common. The model developed here is basically
directed at rat-level problem solving, but is also applicable to AI prob
lems such as checker playing.
An extensive bibliography is provided, not so much to "prove" the de
tails of the implementation as to identify and provide pointers to related
areas of research that can be productively viewed from the perspective of
the model. (Not surprisingly, the chosen references are generally suppor
tive, though). The primary goal of this book is to assemble a functioning