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The Role of analogy in cognitive science Sedighe Hematpoori Farokhy Email: [email protected] 1 Abstract: 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: 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. 1. PREDICTIVE ANALOGY 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]. 2. PROPORTIONAL ANALOGY 2 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. Practicality: 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. Refrences: [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 Canada,2008. 3 4