Blank, Doug

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Scholarship;  Research;  and Creative Work at Bryn Mawr College

Bryn Mawr College
Scholarship, Research, and Creative Work at Bryn Mawr
College
Computer Science Faculty Research and
Scholarship Computer Science
1996
Behavior-Based Analogy-Making
Doug Blank
Bryn Mawr College, dblank@brynmawr.edu
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Custom Citation
Blank, D.S. (1996). Behavior-Based Analogy-Making. Proceedings of the Eighteenth Annual Cognitive Science Society Conference.
Behavior-Based Analogy-Making
Douglas S. Blank
Department of Computer Science
Indiana University
Lindley Hall 215
Bloomington, IN  47408
blank@cs.indiana.edu
Most everyone, including the experts, would agree that
analogy-making is best defined as a process that creates a
mapping between items in one domain (often called the
source) to “similar” items in another domain (often called
the target). Based on this definition, many researchers have
attempted to model analogy-making by creating a mapping
between two sets of data structures that represent the
domains (Gentner, 1983; Holyoak and Thagard, 1989).
Defining analogy-making as “making a mapping between
domains” creates many assumptions about analogy and how
one might model it. For instance, the traditional view
assumes that there exist pre-structured representations on
which the mapping process operates. It is not at all clear that
this assumption is cognitively plausible (Chalmers et al.,
1995). This view also suggests that not only can the
mapping process be separated from the structure-forming
process, but that it is also distinct from more general
perceptual processes. This, too, seems unlikely. Although
these issues have been partially addressed by Hofstadter and
colleagues (1995), one question remains: How could a
system learn to make analogies?
To create a model capable of learning to make analogies
requires re-thinking some basic assumptions. Traditionally,
“making an analogy” has meant explicitly producing the
entire set of correspondences from one domain to the other.
It is difficult to see how a system could learn to do this.
One possible solution, following the general suggestions
of Maes (1993),  is to frame the task in terms of behavior.
One analogy-making task that can be seen entirely in terms
of  behavior is the “Do this!” task posed by Hofstadter and
French (French, 1992). A boiled-down version of this task
was defined as follows. Consider Figure 1. Imagine an
experimenter pointing to the triangle in the source scene and
saying, “Do this!”  The subject’s task is then to point to the
“same” thing in the target scene. If one perceives the
triangle as “the object that differs on the dimension of
shape,” then one might be inclined to choose the square in
the target scene, as it, too, differs by shape from the two
circles in the target scene. Of course, that is certainly not the
only possible answer.
To learn to make this type of analogy, a recurrent
backpropagation network was created. A variation on
Smolensky’s tensor product representation (1990) was
developed and used as input to the network. These
representations, termed iconic, encode an object’s location
in a scene over a set of local units. A series of these iconic
maps, in turn, encodes an object’s color and shape, such as
blue and square.
The network was trained to identify the figure and ground
of each scene presented to it. The network was first trained
to identify the object being pointed to in the source scene as
the figure, and the remaining source objects as the ground.
Given the first scene as context, the network was then
trained to identify the analogous object from the target as
the figure, thereby completing the analogy.
After being successfully trained on many example
analogies, the network was shown to generalize by
performing well on many novel scenes. Analysis has shown
that the network is capable of learning abstract “concepts”
such as “differs by shape.” Although the current model has
some significant limitations, it does suggest a framework for
a behavioral approach of analogy-making capable of
explaining effects such as “systematicity” (Gentner, 1983)
and unifying analogy-making with more general perceptual
processes.
References
Chalmers, D., French, R., and Hofstadter, D. (1995). High-
level perception, representation, and analogy: a critique
of artificial-intelligence methodology. In Fluid concepts
and creative analogies. Basic Books, New York, NY.
French, R. (1992). Tabletop: An emergent, stochastic model
of analogy-making. Doctoral dissertation, EECS
Department, University of Michigan, Ann Arbor.
Gentner, D. (1983). Structure-mapping: A theoretical
framework for analogy. Cognitive Science, 7(2).
Hofstadter, D. and the Fluid Analogies Research Group
(1995). Fluid concepts and creative analogies. Basic
Books, New York, NY.
Holyoak, K.,  and Thagard, P. (1989). Analogical mapping
by constraint satisfaction. Cognitive Science, 13(3).
Maes, P. (1993).  Behavior-based artificial intelligence. In
Proceedings of the Fifteenth Annual Conference of the
Cognitive Science Society. Boulder, CO.
Smolensky, P. (1990). Tensor product variable binding and
the representation of symbolic structures in connectionist
systems. Artificial Intelligence, 46.
Figure 1. A sample problem adapted from French (1992).


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