Learning from Physical Analogies: A Study in Analogy and the Explanation Process.

274 Pages·2007·5.7 MB·English

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AUTHOR
Falkenhainer, Brian Carl
TITLE
Learning from Physical Analogies: A Study in Analogy
and the Explanation Process.
INSTITUTION
Illinois Univ., Urbana. Dept. of Computer Science.
SPONS AGENCY
Office of Naval Research, Arlington, Va. Personnel
and Training Research Programs Office.
REPORT NO
UILU-ENG-88-1785; UIUCDCS-R-88-1479
PUB DATE
27 Dec 88
CONTRACT
N00014-85-K-0559
NOTE
274p.; Ph.D. Dissertation, University of Illinois at
Urbana-Champaign.
PUB TYPE
Reports - Evaluative/Feasibility (142) --
Dissertations /Theses - Doctoral Dissertations (041)
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MF01/PC11 Plus Postage.
DESCRIPTORS
Computer Software; *Inferences; *Learning Theories;
.
Novelty (Stimulus Dimension); *Physical Environment;
Physics; Problem Solving; Qualitative Research
IDENTIFIERS
*Analogical Reasoning; Contextual Analysis;
*Explanations; Phenomenography; PHINEAS (Computer
Program
ABSTRACT
Analogical reasoning and learning applied to the task
of constructing qualitative explanations for physical phenomena are
the subjects of this investigation. Two issues are addressed. The
first is how analogies are elaborated to sanction new inferences
about a novel situation. This issue is adddressed by contextual
structure-mapping, a knowledge- intensive adaptation of D. Gentner's
structure - mapping theory. This approach presents analogy elaboration
as a map-and-analyze cycle, in which two situations are placed in
correspondence, followed by problem solving and inference production
focused on correspondence inadequacies. The second issue is the
evaluation the quality of a proposed analogy and its use for some
performance task. A theory of verification-based analogical learning
is presented to address the tenuous nature of analogically inferred
concepts, and procedures for increasing confidence in the inferred
knowledge are described. Specifically, it relies on analogical
inference to hypothesize new theories and simulation of those
theories to analyze their validity. It represents a view of analogy
as an iterative process of hypothesis formation, testing, and
revision. These ideas are illustrated via PHINEAS, a computer program
that uses similarity to posit qualitative explanations for
time-varying descriptions of physical behaviors. It builds upon
existing work in qualitative physics to provide a means with which to
describe and reason with theories of the physical world. A 144-item
list of references is included. (TJH)
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REPORT NO. UIUCDCS-R-88-1479
UILU- ENG --88 -1785
LEARNING FROM PHYSICAL ANALOGIES:
A STUDY IN ANALOGY AND THE EXPLANATION
PROCESS
by
Brian Falkenhainer
BEST COPY AVAILABLE
December 1988
This research is supported by the Mice
of Naval Ravairch, Ferzunnel
and Training Research Programs, Contract
No. NO0014-46-11.-0550,
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ruing From Physical Analogies: A Study in Analogy
and the Explanation Process
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.UPPLEMENTARY NOTATION
COSATI CODES
18. SUBJECT TERMS (Continue
on reverse if necessary and identify by block number)
FIELD
GROUP
SUB-GROUP
structure-mapping theory
contextual structure-mapping
10
05
verification-based analogical learning
PHINEAS
STRACT (Continue on revere if
necessary and identify by block number)
ro
make programs that understand and interact
with the world as well as people do,
we must
licate the kind of flexibility people exhibit
when conjecturing plausible explanations
the diverse physical phenomena they
encounter. This process often involves drawing
upon
I r
o ysical analogies - -viewing the situation and its behavior
as similar to familiar phenomena,
Fjecturing that they share analogous underlying
causes, and using the plausible interpre
-
ion as a foothold to further understanding, aralysis,
and hypothesis refinement.
his report investigates analogical reasoning and learning
applied to the task of construct
-
lirqualitative explanations for observed physical
phenomena. Primary emphasis is placed on
central questions. First, how are analogies elaborated
to sanction new inferences about
novel situation? This problem is addressed by
connextual structure-mapping, a knowledge
-
I*naive adaptavi.on of Gentner's structure-mapping theory.
It presents analogy elaboration
a map and analyze cycle, in which two situations
are placed in correspondence, followed
problem solving and inference production focused
on correspondence inadequacies, Second,
AM. -
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19. ABSTRACT (cont.)
how is the quality of a proposed analogy evaluated and used for
some performance
task? A theory of verification-based analogical learning is
presented which
addresses the tenuous nature of analogically inferred
concepts and describes
procedures that can be used to increase confidence. in the inferred
knowledge.
Specifically, it relies on analogical inference to hypothesize
new theories and
simulation of those theories to analyze their validity. It
represents a view
of analogy as an iterative process of hypothesis formation,
testing, and
revision.
These ideas are illustrated via PHINEAS, a program which
uses similarity to posit
qualitative explanations for time-varying descriptions of physical
behaviors.
It builds upon existing work in qualitative physics to provide
a rich environment
in which to describe and reason with theories of the physical world.
@Copyright by
Brian Carl Falkenhainer
1989
r-
LEARNING FROM PHYSICAL ANALOGIES:
A STUDY IN ANALOGY AND THE EXPLANATION PROCESS
BY
BRIAN CARL FALKENHAINER
B.S., Santa Clara University, 1982
M.S., University of Illinois, 1985
THESIS
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy in Computer Science
in the Grauuate College of the
University of Illinois at Urbana-Champaign, 1989
Urbana, Illinois
c
LEARNING FROM PHYSICAL ANALOGIES:
A STUDY IN ANALOGY AND THE EXPLANATION PROCESS
Brian Carl Falkenhainer, Ph.D.
Department of Computer Science
University of Illinois at Urbana-Champaign, 1989
Kenneth D. Forbus, Advisor
To make programs that understand and interact with the world as well
as people do,
we must duplicate the kind of flexibility people exhibit when conjecturing plausible ex-
planations of the diverse physical phenomena they encounter. This process often involves
drawing upon physical analogies
viewing the situation and its behavior as similar to fa-
miliar phenomena, conjecturing that they share analogous underlying
causes, r.nd using the
plausible interpretation as a foothold to farther understanding, analysis, and hypothesis
refinement.
This thesis investigates analogical reasoning and learning applied to the task of
con-
structing qualitative explanations for observed physical phenomena. Primary emphasis is
placed on two central questions. First, how are analogies elaborated to sanction
new infer-
ences about a novel situation? This problem is addressed by contextual structure-mapping, a
knowledge-intensive adaptation of Gentner's structure-mapping theory. It presents analogy
elaboration as a map and analyze cycle, in which two situations
are placed in correspon-
dence, followed by problem solving and inference production focused
on correspondence
inadequacies. Second, how is the quality of a proposed analogy evaluated and used for
some performance task? A theory of verification -bated analogical !earning is presented
which addresses the tenuous nature of analogically inferred concepts and describes
pro-
cedures that can be used to increase confidence in the inferred knowledge. Specifically, it
relies on analogical inference to hypothesize new theories and simulation of those theories to
analyze their validity. It represents a view of analogy as an iterative
process of hypothesis
formation, testing, and revision.
These ideas are illustrated via PHINEAS, a program which uses similarity to posit qual-
itative explanations for time-varying descriptions of physical behaviors.
It builds upon
existing work in qualitative physics to provide
a rich environment in which to describe and
reason with theories of the physical world.
iii
Acknowledgements
I would like to thank Ken Forbus for being
a great advisor, coworker, and friend. He
has had a significant positive impact on my research content and perspective. The
research
environment he has established abounds in information, code, and
resources, enabling one
to think creatively about all the possibilities.
I would also like to thank Dedre Gentner, who has acted
as my co-advisor in this work.
It's a joy to work with someone so eager to debate and listen openly
to alternate lines of
thinking. Her work formed the seed around which this thesis has
gr'wn.
Professors Gerald De Jong, Robert Stepp, and David Wilkins served
on my final exam-
ination committee, providing encouragement and suggestions for
improvements.
This thesis and my sanity have benefited from the intuitions and comradery
provided by
the other members of the qualitative reasoning
group. John Collins, Dennis DeCoste, John
Hogge, Paul Nielsen, Gordon Skorstad, Janice Skorstad, and Barry
Smith have stimulated
many interesting ideas. Special thanks is due John Collins for his inspirational conversation
and unique insight. He has both directly and indirectly affected the
content and quality of
this thesis and is th source for several of the
more innovative analogy examples.
Dennis Decoste also deserves special credit for providing
Dan, and for adding f .atures
vital to my specific needs. It's unusual to be able to
say "tell me what to type" and an
hour later have a new, fully functional module in
your system, with never a single error!
Over the years I have learned much from interacting with the EBL/SDBL
community
led by Professors Gerald DeJong and Robert Stepp. Special thanks
go to Scott Bennett,
Steve Cnien, Lawrence Holder, Ray Mooney, Shankar Rajamoney,
Bob Reinke, Bharat
Rao, Jude Shavlick, and Brad Whitehall.
I would also like to thank Shankar Rajamoney for showing
me new facets of the theory
development problem. This had
a significant and benefic:al impact on this thesis.
Mark Burstein, Russell Greiner, Paul O'Rorke, Stuart Russell, Jeff Shrager, and
Tom
Eskridge, through both published and personal communication, have
influenced this work
and forced me to think more deeply about what it is I'm actually
doing.
I am grateful to Professor Klosinski for introducing
me to the wonders of computer
science and saying "you should
go to Illinois ", Dr. Barker for introducing me to scientific
research, and Jim Gill for introducing
me to AI.
Closer to home, I am most grateful to my parents and grandparents for
their support,
encouragement, and faith in me throughout the
years. They are always only a phone call
away.
iv
And finally, my love and gratitude go to my wife Sue, who has supported this thesis
well beyond the call of duty. I hope to now make up for the long hours I have been at work
over the last few years.
This research has been supported by an IBM Graduate Fellowship, two University of
Illinois Cognitive Science / Artificial Intelligence summer fellowships, and by the Office of
Naval Research, Contract No. N00014-850559. I am grateful for their support.
:r
v
Table of Contents
Learning from Physical Analogies
1
1
Explanation
1.1
3
Analogy
1.2
8
Physical Analogies
1.3
14
PHINEAS
1.4
18
Reader's Guide
1.5
19
Similarity Comparisons and Analogical Processing
2
22
The Analogy Process
2.1
22
Structure Mapping Theory
2.2
26
Limitations of Structure-mapping Theory
2.3
28
2.4
Contextual Structure-Mapping
31
The Structure Mapping Engine
3
44
Representation Overview
3.1
45
3.2 SME Algorithm Overview
47
3.3
Modeling Contextual Structure Mapping
55
Analysis
3.4
61
Access
4
69
4.1
Accessing Physical Analogies
70
Implementation
4.2
73
4.3
Disappearing Alcohol Example
80
Perspective
4.4
81
Mapping and Transfer
5
85
Overview
5.1
85
Contextual Structure-Mapping
5.2
88
Transfer
5.3
92
Verification-Based Analogical Learning
6
110
Qualitative Reasoning
6.1
112
Determining the adequacy of
6.2
a model
116
Examples
6.3
118
vi
IO