Principles of Synthetic Intelligence PSI: An Architecture of Motivated Cognition (Oxford Series on Cognitive Models and Architectures)

401 Pages·2009·3.72 MB·English

by  Joscha Bach

Principles of Synthetic Intelligence
Oxford Series on Cognitive Models and Architectures
Series Editor
FRANK E. RITTER
Series Board
RICH CARLSON
GARY COTTRELL
PAT LANGLEY
RICHARD M. YOUNG
Integrated Models of Cognitive Systems
Edited by WAYNE D. GRAY
In Order to Learn: How the Sequence of Topics Infl uences Learning
Edited by FRANK E. RITTER, JOSEF NERB, ERNO LEHTINEN,
AND TIMOTHY M. O’SHEA
How Can the Human Mind Occur in the Physical Universe?
By JOHN R. ANDERSON
Principles of Synthetic Intelligence. PSI: An Architecture
of Motivated Cognition
By JOSCHA BACH
Principles of Synthetic Intelligence
PSI: An Architecture of Motivated Cognition
Joscha Bach
Based on a doctoral dissertation submitted at the Institute
for Cognitive Science, Fachbereich Humanwissenschaften,
Universität Osnabrück.
Cover illustration by Alex Guillotte.
1
2009
3
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Copyright © 2009 by Joscha Bach
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Library of Congress Cataloging-in-Publication Data
Bach, Joscha.
Principles of synthetic intelligence : PSI, an architecture of
motivated cognition / by Joscha Bach.
p. cm.—(Oxford series on cognitive models and architectures ; 4)
Includes bibliographical references and index.
ISBN 978-0-19-537067-6 (cloth : alk. paper)
1. Cognition. 2. Artifi cial intelligence. I. Title.
BF311.B23 2009
153—dc22
2008043048
9 8 7 6 5 4 3 2 1
Printed in the United States of America
on acid-free paper
Foreword
In this book Joscha Bach introduces the MicroPSI architecture, an
integrated model of the mind. It is a broad and shallow architecture
based on situated agents. It implements Dietrich Dörner’s PSI theory.
MicroPSI has several lessons for other architectures and models. Most
notably, the PSI architecture includes drives and thus directly addresses
questions of motivation, autonomous behavior, and emotions. MicroPSI
suggests how emotions arise, and how drives and emotions are different.
Including drives also changes the way that the architecture works on a
fundamental level, providing an architecture suited for behaving auton-
omously, which it does in a simulated world. PSI includes three types
of drives, physiological (e.g., hunger), social (i.e., affi liation needs), and
cognitive (i.e., reduction of uncertainty and expression of competency).
These drives routinely infl uence goal formation and knowledge selec-
tion and application. The resulting architecture generates new kinds of
behaviors, including context-dependent memories, socially motivated
behavior, and internally motivated task switching. This architecture
illustrates how physiological drives and emotions can be included in an
embodied cognitive architecture.
The PSI architecture, while including perceptual, motor, learn-
ing, and cognitive processing components, also includes several novel
knowledge representations: temporal structures, spatial memories, and
several new information processing mechanisms and behaviors, includ-
ing progress through types of knowledge sources when problem solv-
ing (the Rasmussen ladder), and knowledge-based hierarchical active
v
vi Foreword
vision. These mechanisms and representations can also help make other
architectures more realistic, more accurate, and easier to use.
The architecture is demonstrated in a simulated environment, which
was carefully designed to allow and require multiple tasks to be pur-
sued and provides ways to satisfy the multiple drives. MicroPSI would
be useful in its own right for developing other architectures interested
in multi-tasking, long-term learning, social interaction, embodied archi-
tectures, and related aspects of behavior that arise in a complex but trac-
table real-time environment.
The resulting models are as theoretical explorations in the space of
architectures for generating behavior. The sweep of the architecture can
thus be larger than for models of single experiments currently common in
cognitive models. MicroPSI presents a new cognitive architecture attempt-
ing to provide a unifi ed theory of cognition. It attempts to cover perhaps
the largest number of phenomena to date. This is not a typical cognitive
modeling work, but one that I believe that we can learn much from.
Frank E. Ritter
Series editor
Editorial board
Rich Carlson, Penn State
Gary Cottrell, UCSD
Pat Langley, Stanford/ISLE
Richard Young, University College, London
Preface: Building blocks for a mind
“Am I a man dreaming I am a robot, or a robot dreaming I am
a man?”
Roger Zelazny (1965)
The new concept of “machine” provided by artifi cial intelligence is
so much more powerful than familiar concepts of mechanism that
the old metaphysical puzzle of how mind and body can possibly be
related is largely resolved.
Margaret Boden (1977)
This book is completely dedicated to understanding the functional
workings of intelligence and the mechanisms that underlie human
behavior by creating a new cognitive architecture. That might seem like a
piece of really old fashioned artifi cial intelligence (AI) research, because
AI, a movement that set out 50 years ago with the goal of explaining the
mind as a computational system, has met with diffi culties and cultural
resistance.
However, AI as an engineering discipline is still going strong. The
exploration of individual aspects of intelligence such as perception,
representation, memory and learning, planning, reasoning, and behav-
ior control led to tremendous insights and fruitful applications. Results
from AI have brought forth independent fi elds of research as diverse as
computer vision, knowledge management, data mining, data compres-
sion, and the design of autonomous robots. AI as a way of understanding
the mind offers some unique methodological advantages.
vii
viii Preface: Building blocks for a mind
When looking at a system, we might take different stances, as the
philosopher Daniel Dennett suggested (1971): the physical stance,
which attempts a description at the level of the relevant physical entities
(the physical make-up and the governing laws); the design stance (how
the system is constructed); and the intentional stance (a description of
the system in terms of beliefs, desires, intentions, attitudes, and so on).1
Computer science allows taking an active design stance: the one of a con-
structing engineer (Sloman, 2000). Understanding a system in computer
science means one is able to express it fully in a formal language, and
expressing it in a formal language amounts (within certain constraints)
to obtaining a functional model of the thing in question. If the system to
be modeled is a physical system, such as a thunderstorm, then the result
will be a simulation of its functioning. But if we are looking at an infor-
mation processing system that in itself is supervening over its substrate,
then we are replacing the substrate with the implementation layer of our
model and may obtain a functional equivalent.
In recent years, there have been calls from different disciplines—i ncluding
AI, psychology, cognitive neuroscience, and philosophy—to gather under
the new roof of cognitive science and to concentrate on i ntegrative archi-
tectures that are laid out specifi cally for the purpose of modeling and
understanding the human mind. These cognitive architectures, including
EPAM (Gobet, Richman, Staszewski, & Simon 1997); Newell, Laird, and
Rosenbloom’s Soar (1987); and Anderson and Lebière’s ACT (Anderson,
1983, 1990), have become a fl ourishing new paradigm.
Such broad architectures are necessarily shallow at fi rst, replacing
crucial component s with scaffolding and complex behaviors with simple
ones. They have to rely on ideas, paradigms, results, and opinions stem-
ming from many disciplines, each sporting their own—often incom-
patible—methods and terminology. Consequently they will be full of
mistakes and incorrect assumptions, misrepresentations of results,
1 It might be diffi cult to draw a dividing line between the physical stance and the
design stance: the levers and wheels making up a watch are physical entities as well as
parts from an engineer’s toolbox. On the other hand, the lowest levels of description
used in physics are in a state of fl ux. They are competing theories, pitched against each
other with respect to how well they construct observable behaviour. In fact, both the
physical stance and the design stance are functionalist stances, each with regard to a dif-
ferent level of functionality.
Preface: Building blocks for a mind ix
distortions resulting from skewed perspectives, and over-simplifi cations.
Yet, there is reason to believe that, despite inevitable diffi culties and
methodological problems, the design of unifi ed architectures modeling
the breadth of mental capabilities in a single system is a crucial stage in
understanding the human mind, one that has to be faced by researchers
working at the interface of the different sciences concerned with human
abilities and information processing. We will have to put up with the
burden of interdisciplinarity, because the areas of human intelligence
are inseparably intertwined. Language cannot be fully understood with-
out understanding mental representation, representation cannot be
understood without perception, perception not without interaction,
interaction not without action control, action control and affordances
not without motivation, and motivation not without the contexts set by
evolution, environment, physiology, and sociality, and so on. An under-
standing of the mind that does not regard the psychological, the social,
the physiological interface to the world, language, reasoning, emotion,
and memory, and their common ground (which I believe to be informa-
tion processing) will not only be incomplete in isolated parts, but is not
going to be an understanding at all.
In a way, the designer of a unifi ed architecture is in a situation similar to
that of the cartographers who set out to draw the fi rst maps of the world,
based on the reports of traders and explorers returning from expeditions
into uncharted waters and journeys to unknown coasts. These travel-
ers were of very diverse ilk, and their reports were often contradictory,
incomplete, and inaccurate. Coasts of suspected continents turned out
to be islands, small islets turned out to be outcrops of vast lands. What
appeared to be a sea sometimes was just a lake, different places appeared
to be identical, and passages thought to be leading to known lands led
into territory that was previously unheard of. All the while, the cartogra-
phers were sitting at their drawing tables (or were traveling on some boat
themselves), and drew their maps based on their own mistaken precon-
ceptions, which constrained continents into circular shapes with Biblical
settlements in their center.
At the same time, there were geometers and prospectors at work who
did “proper maps” with tools fi t for the task. They charted houses, vil-
lages, roads, and counties, over and over, often with astounding accuracy.
But although their work was irreplaceable for their respective and local