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The Role of analogy in cognitive science
Sedighe Hematpoori Farokhy
[email protected]
Analogy and analogical reasoning play vital role in various cognitive abilities such as
reasoning, creativity and learning. Thus, analogies can be considered as a basis for unified
large-scale cognitive systems. Below we have a brief description of analogy and the different
types of analogies.
Analogy is an integral part of human understanding and problem solving, and thus becomes
an interesting challenge for artificial intelligence[1]. The concept of analogy requires some
ability to perceive likeness between dissimilar objects/abstractions in different domains and
extrapolate a relationship for other situations. As such, analogies may cover a wide array of
concepts, and may require an even larger pool of knowledge from which to draw conclusions
about relationships and attributes. For humans, analogy is usually quite natural, as our
cognitive processes have extensive resources on context, history, and personal experience
from which to draw. The real trick is developing a means for such understanding to be
automated [2].
To perform computational analogy, a mapping must be made of the differences between the
objects in the first domain. Analogical mapping was first introduced by Gentner (1983) as
Structure Mapping Theory (SMT): a mapping devised to a) use relationship matching and b)
make such matching as generalized as possible . The mapping process, occurring between
two domains, consisted of these four steps in reasoning: a) recognition of the source
domain, b) elaboration and evaluation between the two domains, c) transferring of some
similarities between source and target, and d) consolidation of the attributes shared between
domains [3].
The type of these domains can vary significantly, however, and has prompted study into the
diverse ways analogy is made. The below section describes the different types of analogies.
Predictive analogy centres on the reasoning capabilities of the system. To facilitate
reasoning, constraints are made on the domain. Generally these constraints are structural in
nature, but can be more intangible. Some abstract constraints include a) similarity
constraints which narrow the search to identical concepts, b) semantic similarity which gives
preference to mappings that are semantically related and c) pragmatic constraints which
prefer mappings which are relevant to some final goal. To illustrate, consider these three
models of analogical reasoning [4].
The first, the Analogical Constraint Mapping Engine (ACME), picks the mapping between
two domains using structural, similarity, and some pragmatic constraints on a network of
possible mappings. The second, the Incremental Analogy Machine (IAM), uses structural,
similarity, semantic and pragmatic constraints to find mappings of subsets of the domains.
The finally mapping will be picked from these incrementally found mappings. The third, the
Structure-Mapping Engine (SME), uses structural and similarity constraints to find a number
of locally correct mappings, then chooses from these which is the most applicable over both
domains [3].
Unlike predictive analogy, proportional analogy finds mappings between objects within the
same domain, making proportional a special case for predictive analogy. Generally,
proportional analogy takes on the form: A:B :: C:D, meaning that there is some attribute that
relates A and B which similarly related C and D. This type of problem usually solves for D,
given A, B, and C. To do so, A and B are compared to find the source of difference between
them. This transformation is then applied to C to produce D[5][6].
Proportional analogy is further categorized as either a verbal analogy[7] or geometric, which
are the most common methods of testing IQ.
The ability to solve geometric analogy can be very useful in domains such as visual stimuli, object
categorization and recognition, spatial cognition and spatial reasoning which has a core role in
reasoning about locations in the real world through diagrams, maps and schematics.
Verbal analogy and finding the semantic relations between different pair words has a vital rule in text
categorization, word sense disambiguation, machine translation and information extraction.
According to Schwering and her colleges analogy has vital role in invention such ComputerAided Innovation and computer creativity.
[1]. R. Hall, “Computational approaches to analogical reasoning: a comparative analysis,”
Artificial Intelligence, vol. 39, pp. 39-120, 1989.
[2]. A. Schwering, “Analogical reasoning: A core of cognition”, Institute for Cognitive Sciences in
Osnabr¨uck, 2008.
[3] M. T.Keane, “Constrains on analogical Mapping: a comparison of Three Models,”
Cognitive Science, vol. 18, pp. 387-438, 1994.
[4] D. P.Miranker, “Treat: A Better Match Algorithm for AI Production Systems,” in AAAI, 2008.
[5] D. p. .. Emma-claire Mullally, “Spatial inference with geometric proportional analogies,”
Artif Intell Rev, vol. 26, pp. 129-140, 2007.
[6] A. S. a. H. G. a. K.-. U. Kuhenberg, “solving Geometric proportional analogies
with the Analogy Model HDTP,” Institue of Cognitive Science , Osnabruk.
[7] Turney, P. D .“ A Uniform Approach to Analogies, Synonyms, Antony,”. attawa, National Research Council of