Transcript Document

Progress on the
Structure-Mapping Architecture
for
Learning
Dedre Gentner
Kenneth D. Forbus
Northwestern University
Symbolic modeling crucial for understanding
cognition
• Heavy use of conceptual knowledge is a signature
phenomena of human cognition
– People understand, make, compare, and learn from
complex arguments
– People learn conceptual knowledge from reading texts,
and apply what they have learned to new situations
– People reason and learn by analogy, applying
precedents and prior experience to solve complex
problems
– People use symbolic systems (e.g., language, maps,
diagrams)
• Symbolic models remain the best way to explore
many conceptual knowledge issues
Overview
• Structure-Mapping Architecture
• Accelerating learning via analogical encoding
– Brief review
• Tacit analogical inference
– Analogy on the sly
• Similarity-based qualitative simulation
• Transfer and outreach activities
Structure-Mapping Theory (Gentner, 1983)
• Analogy and similarity involve
– correspondences between structured descriptions
– candidate inferences fill in missing structure in target
Inference
is selective.
Not all base
knowledge is
imported
Candidate
Inference
completes
common
structure
• Constraints
– Identicality: Match identical relations, attributes, functions. Map non-identical
functions when suggested by higher-order matches
– 1:1 mappings: Each item can be matched with at most one other
– Systematicity: Prefer mappings involving systems of relations, esp. including
higher-order relations
Analyzing similarities
and differences,
reasoning from
experience, applying
relational knowledge
Functional Overview
US
Israel
Iraq
Iran
WMD
Nuclear Reactor
Invasion
Bombing
SEQL
Similaritybased retrieval
of relevant
examples and
knowledge
Potentially
relevant
precedents
SME
Incrementally constructs
generalizations,
producing human-like
relational abstractions
within similar number of
examples.
MAC/FAC
Long-term memory
Psychological Studies
1. Case-comparison method
Previous work: Transfer
New work: Learning of principles
2. Unaware analogical inference
Previous work: Unaware inference
New work: Attitude congeniality & unaware
inferences
New work: Unaware alignment-based decision
making
Analogy
• Core process in higher-order cognition
• A general learning mechanism by which complex
knowledge can be acquired
• e.g., causal structures & explanatory principles
• Unique to humans (or nearly so):
Similarity
Species-general
Analogy
Species-restricted
A
A
AA
B
Object match
BB
CD
Relational match
Analogical Encoding in Learning
Standard analogical learning:
• Analogy can promote learning
– Induces structural alignment
– Generates candidate inferences
Familiar
Situation
Inferences
New
Situation
• But, memory retrieval of potential
analogs is unreliable
Inert knowledge: Learned material often
fails to transfer to new situations
Analogical encoding:
• Solution: Analogical encoding
Use comparison during learning to
- highlight the common relational
system
- promote relational abstraction &
transfer
New
Situation
Compare
New
Situation
Relational
Schema
New
Situation
Case Comparison Method in Learning to Negotiate
Studies of MBAs learning negotiation strategies
Students study two analogous cases prior to negotiating
Loewenstein, Thompson & Gentner, 1999
Thompson, Gentner & Loewenstein, 2000
Gentner, Loewenstein & Thompson, 2003
case 1
case 2
Separate Cases Condition
Read each case, write principle
and give advice.
Comparison Condition
Compare the two cases and
write the commonalities
Simulated
Negotiation
On a new analogous case
Negotiation transfer performance across three studies:
Proportion using strategy exemplified in the cases
Prop. Forming Contingent Contracts
.8
.7
.58*
.6
.5
.4
.3
.2
.19
.24
.1
0
No Cases
Separate Cases
Compare
N=42
N=83
N=81
Better schemas  Better transfer
Prop. Forming Contingent
Contracts
.8
.7
Compare
.6
.5
.4
Separate
Cases
.3
.2
.1
0
0
0.5
1.0
1.5
2.0
Dyadic Schema Rating
So, what happens if we just give them the principle?
Prop. Forming Contingent Contracts
Aligning case and principle improves ability to use
principle in transfer
.7
Separate
Principle
Plus case
.6
.5
.44*
Case 1
________
________
Case 2
.4
___________
___________
Compare
Principle
and Case
Case 1
Case 2
__________
____________
____________
_________
.3
.2
Test: Face-to-face negotiation
.19
.1
0
Separate Cases
N=26 dyads
Compare
N=27 dyads
Error bars assume binomial with prop=.19 (baseline)
Comparison promotes transfer even when the
principle is given - Why?
Principles utilize abstract relational language
• Relational language—verbs, prepositions, relational nouns—
is contextually mutable  interpretation difficulties
– e.g., force in physics =/= force in commonsense language
• Assembling a complex relational structure is errorful
• So, beginning learners don’t understand principles when
presented solo
Case provides a firm relational structure that is correct but
overly specific
– learning is context-bound – strongly situated
– So unlikely to transfer
Comparing a principle and a case
– grounds the principle in a firm structure
– invites abstracting the specific relations in the case
Learning Negotiation Principles- Experiment 1
Training:
• participants read two passages
– a negotiation principle (Contingent Contract)
– an analogous case
• Separate condition: Participants consider each passage separately.
• Compare condition: Participants consider how the case and principle are
alike.
• Two orders: caseppl and pplcase
• All participants answer the question
"How could this be informative for negotiating?"
20-minute delay
Test: Recall task: subjects write out the principle they learned
Principle: Contingent Contract
A contingent contract is a contract to do or not to do something
depending on whether or not some future event occurs. At least two
kinds of situations exist in which contingent agreements add potential
for joint gains – when disagreeing over probabilities and when both
parties try to influence an uncertain outcome. When the uncertain
event itself is of interest, there are familiar economic contingent
contracts with “betting” based on the probability of differences.
Parties are dealing with uncertain quantities and actually or apparently
differ in their assessment, and here contingent arrangements offer
gains. When the parties feel capable of influencing an uncertain event,
making the negotiated outcome dependent on its resolution may be a
good idea. In both cases of course, contingent arrangements based on
underlying differences are not a panacea. Crafting them effectively
can be a high art. And once the outcome of the uncertain event is
known, one party may have “won” and the other “lost.” Whether the
outcome will then be considered fair, wise, or even sustainable is an
important question to be planned for in advance.
Training Case
Two fairly poor brothers, Ben and Jerry, had just inherited a working
farm whose main crop has a volatile price. Ben wanted to sell rights to
the farm’s output under a long-term contract for a fixed amount rather
than depend upon shares of an uncertain revenue stream. In short, Ben
was risk-averse. Jerry, on the other hand, was confident that the next
season would be spectacular and revenues would be high. In short,
Jerry was risk-seeking. The two argued for days and nights. Ben
wanted to sell immediately because he believed the price of the crop
would fall; Jerry wanted to keep the farm because he believed the price
of the crop would increase.
Finally, Jerry proposed a possible agreement to his brother: They
would keep the farm for another year. If the price of the crop fell
below a certain price (as Ben thought it would), then they would sell
the farm and Ben would get 50% of the farm’s current value, adjusted
for inflation; Jerry would get the rest. However, if the price of the
crop were to rise (as Jerry thought it would), Jerry would buy Ben out
for 50% of the farm’s current value, adjusted for inflation, and would
get to keep all of the additional profits for himself. Jerry was delighted
when his brother told him he could agree to this arrangement, thereby
avoiding further conflict.
Recall Scores (Max. = 8)
Gentner & Colhoun
4.0
Mean Recall Score
3.5
3.4
3.0
2.5
2.5
2.0
1.5
1.0
0.5
0.0
Separate
Two blind raters
Agreement: 94%
(26)
C om pare
Condition
(26)
t(50) = 2.10, p = 0.041
Quotes from the Compare Group
• P18: "Contingent contract Principle: if there is an uncertain
event occurring in the future which two parties disagree
on, the outcome of this event becomes the determining
factor in the outcome of the negotiation."
• P30: "The contingency contract is created as an agreement
to do/not do something in the future in the event of a
situation. As the future is unknown, the CC is created on
the probability that something will occur…"
Quotes from the Separate Group
•P50: "It is important to consider how much you will lose or win
when betting on an uncertain event. Negotiating in this situation is
more complicated than just predicting the outcome." (this was the
entire answer)
•P51: "We read about the two poor brothers on the farm. One was
risk-seeking and the other was risk-seeking, so they couldn't decide
on whether or not to sell the farm…" (no mention of the principle)
Delayed Recall
Read Principle & Case
(20 mins)
Immediate Recall
Test case: Asian Merchant
N=14; 7 sep, 7 comp
(4 days)
Long term Recall
New test case
N=14; 7 sep, 7 comp
Immediate Recall Scores
Both Orders
6
Mean Recall Score
5
4.4
4
3
3.1
2
1
0
Separate
(11)
C om pare
(12)
T(23) = -2.44, p = 0.023
Delayed Recall Scores
Both Order
6
Mean Recall Score
5
4.3
4
3
3.1
2
1
0
Separate
(11)
C om pare
(12)
T(21) = -1.91, p = 0.07
Combines two groups with slightly different procedures
Conclusions
Comparison group > Separate group
Case-first groups > Principle-first groups
Comparing case and principle greatly
benefits comprehension of principle
The case provides firm relational structure
and a clear (though overly specific)
interpretation of the relational terms
Comparing case with principle prompts rerepresentation and abstraction of the
relational structure
Practical Implications
• Case-based training is heavily used in professional
schools (business, medicine, law) – intensive
analyses of single cases
– Our results suggest that learning could be greatly
increased by changing to a comparison-based
instructional strategy
• Based on our findings, some institutions are
revising their instructional methods
– Medical School of McMaster University
• Developing a new curriculum relying heavily on comparisonbased instruction
– Harvard Business School
• Exploring comparison-based method
– CMU – discussions with Marsha Lovett
Unaware Analogical Inference
Analogy as generally conceived:
Current Studies:
• Conscious
• Non-aware
• Discerning
• Oblivious
• Deliberate
• Non-deliberate
• Effortful
• Accidental
Suggestive evidence: Blanchette & Dunbar, 2002; Moreau, Markman & Lehman, 2001
New thrust: Study of “unwitting analogy”
• Can analogical inferences occur without awareness of
making the inferences?
• Can analogical inferences occur without awareness of
the analogy itself?
• Can the highlighting effect of analogical alignment
Influence future decision-making?
Analogical insertion effect:
believing that the analogical inference from
BT actually occurred in T
•
Evidence for analogical insertion
Blanchette & Dunbar 1999
Analogy: Anti-marijuana laws are like Prohibition
Participants misrecognized parallel inferences as having occurred
in marijuana passage
• But, these pro-marijuana inferences were likely to be
congenial to college students
• Will analogical insertion occur if the inference is not
so congenial?
Attitudes towards gayness assessed (Mass testing)
3-4 weeks (unrelated context)
Read paragraph “Is it OK to be gay”
Analogy group
Second paragraph analogizing
gayness to left-handedness
Control group
No further text
15-min filled delay
Old-New recognition test
Rate soundness of analogy
Perrott, Gentner &
Bodenhausen, 2005
Proportion of "old" responses
Proportion “old” responses
Perrott, Gentner &
Bodenhausen, 2005
1
Analogy
0.8
No-Analogy
0.6
*
0.4
0.2
0
Text Item
Analogical
Inference
Plausible
False Item
Blatantly False
Item
Condition(2) X Item type(4)
F(3, 228) = 4.97, p= .002, MSE = .048
Results within analogy group
Attitudes towards gays within predicted the rated soundness of
the analogy
But
Likelihood of analogical insertion was not predicted by rated
soundness of the analogy
Even more surprisingly,
Likelihood of analogical insertion was not predicted by
attitude towards gays – No “attitude congeniality effect”
Attitudes measured on 15-item questionnaire  composite scale from
1 (very negative) to 7 (very positive). Range: 1.8-6.8 (M = 4.7)
Cutoffs for lower and upper quartiles = 3.3 and 5.8
Can analogical insertion occur without
awareness of the analogy
• Participants read a series of passages
• Told that they would be asked questions about
content of passages
• We observed extent to which analogous
passages early in the set influenced the
interpretation of later passages
• No goal other than comprehension
• Inferences support understanding the input
Day & Gentner; 2003, in prep
Current Studies
• Participants read a series of passages
• Some early passages are relationally similar to later
passages
• Will participants use structure-mapping in
interpreting the later passages?
TEST:
• Participants answer TF questions about passages
• Dependent measure: Answering True to questions that are
inferences from earlier analogous passages.
Experiment 1
Two versions of
each base passage
• If participants use analogical inference
from the earlier similar base passage,
they will understand the target
differently, depending on which base
version they got.
Target has some
ambiguous portions
Example Source Passages
Base 1:
Base 2:
Wealthy elderly woman dies
mysteriously
Her niece respectfully flies into town
for the funeral
Wealthy elderly woman dies
mysteriously
Her niece suspiciously leaves town
when the death is announced
People are surprised when the will
leaves everything to the niece
People are surprised when the will
leaves everything to the niece
Target Passage:
Wealthy elderly man dies mysteriously
As soon as the death is announced, the man’s
nephew immediately buys a ticket and flies to
Rio de Janeiro
People are surprised when the will leaves
everything to the nephew
Expt. 1 Results: More false recognitions for baseconsistent statements
Percentage ‘yes’ responses
100
73%
50
25%
0
Base-consistent
Base-inconsistent
Using base consistency as a
within-subjects factor
Day & Gentner, 2003
t (19) = 4.79, p < .001
E1
Results: Analogical insertion
• P’s interpreted the ambiguous portion of the target in a
manner consistent with structurally matching information in
the base.
• The same target passage was interpreted differently, as a
function of which base P’s had read
• Evidence suggests that analogical inference influences the
interpretation of new material
• Not due to deliberate strategies:
90% noticed similarities between passages
But, 80% said all passages were understandable on their own.
• Not due to simple priming: further study showed inferences
are specific to the structural role of the inserted information
Experiment 3
Is the analogical insertion effect occurring
during online comprehension of target, or is it a
later memory error?
Experiment 3: Self-paced Reading Task
• Base passage and target passage same as in Expts 1 and 2,
except:
• Target contains a later key sentence that is consistent with
one base’s inference and inconsistent with the other’s:
“George's absence from the service was
conspicuous, especially since he had been
seen around his uncle's estate prior to his
death, and the police soon found out about
his flight to Rio.”
If P’s insert the seeded inference into
the target story, they will take longer
to read the key test sentence when it is
inconsistent with that inference
Experiment 3: Self-paced Reading Task
• Base passage and target passage same as in Expts 1 and 2,
except:
• Target contains a later key sentence that is consistent with
one base’s inference and inconsistent with the other’s:
Results
If P’s insert the seeded inference into
the target story, they will take longer
to read the key test sentence when it is
inconsistent with that inference
F (1,19) = 6.81, p < .05
10
8.88
9
Reading time (sec)
“George's absence from the service was
conspicuous, especially since he had been
seen around his uncle's estate prior to his
death, and the police soon found out about
his flight to Rio.”
8
7
6.40
6
5
4
BaseBaseconsistent inconsistent
Tacit analogical inferences
Day & Gentner; 2003, in prep
• People interpolated analogical inferences from a prior similar
passage
due to shared representational structure, not simply to
general priming
• Implication: Structure-mapping can operate in nonaware,
non-deliberative processing
• But –what about large number of analogy studies that show
failure to transfer ?
Current studies
•Vary delay: 20 minute vs. 4 days later
•Vary surface similarity between the passages
• Future work: Progressive alignment effect? Does an obvious
alignment potentiate more analogical creep?
Day & Bartels (2005)
Unaware effects of analogy: Decision-making
Structure mapping theory proposes that comparison involves the alignment of
representational structures (Gentner, 1983; Gentner & Markman, 1997)
This implies two kinds of differences:
alignable differences: different values on same predicate or dimension;
related to common structure
non-alignable differences: none of the above
Alignable differences are weighted more heavily in
perceived similarity (Markman & Gentner, 1996)
difference detection (Gentner & Markman, 1994)
recall (Markman & Gentner, 1997)
preference (e.g., Roehm & Sternthal, 2001)
Hypotheses:
Alignment along a dimension renders that dimension more salient in immediate use
Repeated alignment & use renders the dimension more salient in future encodings
Method: P’s choose among portable digital video players
1. First, participants gave preference ratings for models that
varied on only one alignable dimension:
Firewire and USB connectivity:
Battery life:
Voice recorder:
Hard drive capacity:
Built-in FM radio:
Wireless projection range :
Support for WMV and MP2 formats:
Screen size:
Weight:
Strongly prefer
Model A
Model A
Yes
4 hr
No
7 Gb
Yes
12 ft
No
2.5 in
10 oz
Model B
Yes
4 hr
No
4 Gb
Yes
12 ft
No
2.5 in
10 oz
Strongly prefer
Model B
Method
1. First, participants gave preference ratings for models that
varied on only one alignable dimension:
Firewire and USB connectivity:
Battery life:
Voice recorder:
Hard drive capacity:
Built-in FM radio:
Wireless projection range :
Support for WMV and MP2 formats:
Screen size:
Weight:
Strongly prefer
Model A
Model A
Yes
4 hr
No
7 Gb
Yes
12 ft
No
2.5 in
10 oz
Model B
Yes
4 hr
No
4 Gb
Yes
12 ft
No
2.5 in
10 oz
Strongly prefer
Model B
Method
2. Eventually, they make judgments between models
varying on two dimensions, each favoring a different alternative
Firewire and USB connectivity:
Battery life:
Voice recorder:
Hard drive capacity:
Built-in FM radio:
Wireless projection range :
Support for WMV and MP2 formats:
Screen size:
Weight:
Strongly prefer
Model A
Model A
Yes
4 hr
No
10 Gb
Yes
12 ft
No
1.5 in
10 oz
Model B
Yes
4 hr
No
7 Gb
Yes
12 ft
No
2.5 in
10 oz
Strongly prefer
Model B
Experiments
Experiment 1
• Are more recently used dimensions weighted more
in future decisions?
That is, does aligning a dimension make it more salient
for some period of time?
Experiment 2
• Are dimension that have been used more frequently weighted
more in future decisions?
That is, does repeated alignment along a dimension
render that dimension more salient in future encodings?
Types of item series
1 back:
Diagnostic dimension
A B C D E
- - - - - - - -
1 v. 2 back:
Diagnostic dimension
A B C D E
- - - - - - - - - -
1 v. 3 back:
2 v. 3 back:
Diagnostic dimension
A B C D E
- - - - - - - - - - - -
Diagnostic dimension
A B C D E
- - - - - - - - - - - -
Results
Day & Bartels (2005)
Experiment 1
• Each response was coded as a value between 0 and 1
• .5 would be chance; averages closer to 1 indicate a
preference for the more recently diagnostic dimension
Average response was .62 (p < .001)
18 out of 20 participant had average ratings greater than .5
Participants weighted a dimension more if it had been
used in a more recent decision
Results
Day & Bartels (2005)
Experiment 2
• Found correlation between preference ratings and number
of prior uses of a dimension for each participant
• Individual correlations transformed into Fisher’s Z for use
in analysis
Average transformed correlation was .20 (p < .01)
Participants weight a dimension more if it had been
used more frequently in prior decisions
Conclusions
Day & Bartels (2005)
• Finding an alignable difference along a dimension
makes that dimension more salient for a period of time
more recently aligned dimensions play a larger
role in future decisions
• Repeated alignment of a dimension increases its
salience in future encodings
higher numbers of repetitions  greater
dimension weights in decisions
• These effects of comparison may go unnoticed, but
may have pervasive effects on the mental landscape
Resistance is futile
• Analogical insertion—interpolation of inferences into the
target situation—can occur
• when an analogy is given explicitly
• when an alignable analog has been presented recently
• Online comparisons increase the salience of aligned
dimensions for future encodings
• Hypothesis: Continual subtle learning occurs via
structural matching and inference
• Fits with MAC/FAC assumption of continual unbidden
retrieval
•
• Challenges & Future work:
• How recent?
• How similar and in what ways?
• Effects of intervening items?
How do people do common sense reasoning?
• Today’s methods of qualitative reasoning are very
useful
– Many successful applications in engineering, education,
supporting scientific reasoning
• Are they also good models of how people
common sense reasoning?
– Yes, but similarity plays major role in reasoning
• Important question for cognitive science
– Central to understanding mental models
The standard Qualitative Reasoning community answer
Scenario model
Situation description input
F
G
Model Builder
H
F
Qualitative
simulation
F GH
1st principles
Domain Theory



G

H
Qualitative
Simulator
F G H
i
FGH
i
FGH
FG H
i
FGH
F G H
First-principles qualitative simulation
Useful properties
Problematic properties
• Handles incomplete and
inexact data
• Supports simple
inferences
• Explicit representation of
causal theories
• Exclusive use of 1stprinciples domain theory
– To prevent melting, remove
kettle from stove
• Representation of
ambiguity
– We easily imagine multiple
alternatives in daily
reasoning
– inconsistent with psychological
evidence of strong role for
experience-based reasoning
• Exponential behavior
– inconsistent with rapidity &
flexibility of human
reasoning
• Generates more complex
predictions than people
report
– logically possible, but
physically implausible
Working hypotheses about human common sense
reasoning and learning
(Forbus & Gentner, 1997)
• Common sense = Combination of analogical reasoning
from experience and first-principles reasoning
• Within-domain analogies provide robustness, rapid
predictions
– Human learning requires accumulating lots of concrete examples
– Structured, relational descriptions essential – feature vectors
inadequate
• First-principles reasoning emerges slowly as
generalizations from examples
– Human learning tends to be conservative
– But human learning also tends to be faster than pure statistical
learning
• Qualitative representations are central
– Appropriate level of understanding for communication, action, and
generalization
An alternative: Hybrid qualitative simulation
• Most predictions, explanations generated via
within-domain analogies
– Provides rapidity and robustness in common cases
– Multiple retrieved behaviors leads to multiple
predictions.
– Logically possible behaviors that are rarely observed
aren’t predicted.
• 1st principles reasoning relatively rare
– 1st principles domain theories fragmentary, partial
• Some 1st principles knowledge created by generalization over
examples
• Much of it taught via language
• We built a similarity-based qualitative simulator
to explore this approach
A Prototype SQS System
Situatio
n
MAC/FAC
Experience
Library
Rerep
Engine
SEQL
Candidate
Behaviors
Projector
Predictions
Experience Library Contents
• Current sources
– Classic QR examples
• Generated envisonments using Gizmo Mk2
– Feedback systems
• Generated descriptions of behavior by hand
• Test of whether system can operate without a complete 1stprinciples domain theory
• Each case consists of a qualitative state
– Individuals, ordinal relations, model fragments
– Concrete information about entities (stand-in for perceptual
properties)
– Description of transitions to other states
Example: Two Containers Liquid Flow
State0
State1
↓(AmountOf Water Liquid F)
↑(AmountOf Water Liquid G)
↓(Pressure Wf)
↑(Pressure Wg)
(> (Pressure Wf)
(pressure Wg))
(activeMF LiquidFlow)
State2
↑(AmountOf Water Liquid F)
↓(AmountOf Water Liquid G)
↑(Pressure Wf)
↓(Pressure Wg)
(< (Pressure Wf)
(pressure Wg))
(activeMF LiquidFlow)
→(AmountOf Water Liquid F)
→(AmountOf Water Liquid G)
→(Pressure Wf)
→(Pressure Wg)
(= (Pressure Wf)
(pressure Wg))
(not (activeMF LiquidFlow))
Input Scenario
↓(AmountOf Water Liquid Beaker)
↑(AmountOf Water Liquid Vial)
↓(Pressure Wb)
↑(Pressure Wv)
(> (Pressure Wb)
(pressure Wv))
→(AmountOf Water Liquid Beaker)
→(AmountOf Water Liquid Vial)
→(Pressure Wb)
→(Pressure Wv)
(= (Pressure Wb)
(pressure Wv))
Behavior Prediction
Example: Heat Flow
State0
State1
↓(Temperature Coffee)
↑(Temperature IceCube)
(> (Temperature Coffee)
(Temperature IceCube))
(activeMF HeatFlow)
→(Temperature Coffee)
→(Temperature IceCube)
(= (Temperature Coffee)
(Temperature IceCube))
(not (activeMF HeatFlow))
Retrieved
analogue
Input
Scenario
Input Scenario
Predicted Behavior
↓(Temperature Brick)
↑(Temperature Water)
(> (Temperature Brick)
(Temperature Water))
(activeMF HeatFlow)
→(Temperature Brick)
→(Temperature Water)
(= (Temperature Brick)
(Temperature Water))
(not (activeMF HeatFlow))
Example: Discrete action feedback system
Mappings for Feedback Example
Feedback Control System
Water Level Regulation System
Sensor
Floating ball
Comparator
Ball Stick
Controller
String + Pulleys
Actuator
Valve
Temperature set point
Proper water level
Room air
Tank water
Room
Water tank
Oven
Water supply
Heat flow process
Liquid flow process
Furnace on process
Valve open process
Stored Feedback System Behavior
Quantities
S1
S2
S3
S4
S5
S6
(Temperature
Room)vs.SetPoint
<
=
>
>
=
<
(Ds (temperature Room))
1
-1
(activeMF FurnaceOn)
Yes
No
(activeMF HeatFlow)
Yes
Yes
Retrieved Behavior
S1
S6
S2
S5
S3
S4
Mapped Feedback System Behavior
Retrieved Behavior
Quantities
S1
S2
S3
S4
S5
S6
(Temperature
Room)vs.SetPoint
<
=
>
>
=
<
(Ds (temperature Room))
1
-1
(activeMF FurnaceOn)
Yes
No
(activeMF HeatFlow)
Yes
Yes
Quantities
S1
S2
S3
S4
S5
S6
(Level TankWater) vs.
ProperWaterLevel
<
=
>
>
=
<
(Ds (Level TankWater))
1
-1
(activeMF ValveOpen)
Yes
No
(activeMF LiquidFlow)
Yes
Yes
Predicted Behavior
Example: Proportional action control system
• Amount of correction applied is proportional to
the error signal
• SQS prototype with current library makes
incorrect prediction
– Retrieves discrete-action controller behavior
– Currently has no means of detecting inconsistencies
• Possible solutions
– Include some first-principles reasoning for reality
checks
– When failure detected, add new behavior to Experience
Library to improve future performance
Current Issue: Combining Behaviors
P(Wg) 
 P(Wf)
F
Two
mappings,
how to
combine?
A
G
B

Level(Wf)
Level(Wg)
FR(FG)
Q+

G
Aof(Wg)
I-

Q+
Aof(Wf)
A
H
F
Q+
Q+
I+
H
B

Pastiche Mappings
• Retrieve behaviors for unexplained parts of system
• Combine by re-evaluating closed-world
assumptions
Perform influence
resolution to combine
influences across
cases
F
G
H
Next steps: Hybrid qualitative simulation
• Significantly expand Experience Library
– Plan: Use EA NLU system to describe qualitative states in
QRG Controlled English
• Test skolem resolution strategies
– Identify hypothesized entities with unmapped current
situation entities when possible.
• Formulate criteria for using multiple remindings
– When to generate alternate predicted behaviors?
• Develop more selective rerepresentation strategies
– Currently performed exhaustively
• Explore learning strategies
– Store rerepresented results and new behaviors
– Use SEQL to construct generalizations
Geometric Analogy Problems
• Evans classic 1968 work ANALOGY
– Miller Analogies Test geometric problems
– Non-trivial human intelligence task
• Goal of our simulation:
– Show that general-purpose simulations can handle this task
– Another source of data for tuning visual representations in
our sketching system
Sketching the Geometric Analogy Problems
A
1
B
2
C
3
4
5
Finding the Answer: Evans
A is to B as C is to 1, 2, 3, 4 or 5?
• Compute all transformations AgB, Cg1, Cg2, …
• Search for best match between transformation for
AgB with all of the transformations for Cg1, Cg2,
…
Finding the Answer: Our simulation
A is to B as C is to 1, 2, 3, 4 or 5?
A
B
SME
AB
SME
C1
Differences
compared at
second level
C
1
SME
...
...
...
...
5
SME
C5
Answer 1
SME
Answer 5
Two-stage structure mapping
Results
(Based on Evans’ answer key)
MAT Problems
ANALOGY
sKEA/SME
1-9,11, 13-18, 20 Correct
Correct
10
Incorrect
Correct
12
Correct (prefers
reflection)
Correct (prefers
rotation)
Incorrect (prefers
rotation)
Correct (prefers
rotation)
19
Problem case 12
Summary: Geometric Analogies Simulation
• SME + qualitative spatial representations provide a
basis for solving geometric analogy problems
• Two-stage structure mapping provides an elegant
model for this task
– Explicit transformation rules unnecessary
– Applicable to other analogy tasks?
Future Directions
THE END