Abstract
Analogy is the cognitive process of matching the characterizing features of two different items. This may enable reuse of knowledge across domains, which can be helpful to solve problems. Analogy is strongly related to semantics, because the mappings are based on the role and meaning of the features, which goes beyond simple syntactic association. The analogical mappings found between pairs of descriptions can be used to obtain more general analogical patterns. Such patterns may be stored in the long term memory, allowing self-improvement and growth. This paper proposes generalizations of patterns obtained by analogy, carried out through two main steps: (1) isolating analogous roles of two descriptions coming from different domains, and (2) abstracting from portions of knowledge that have no analogical relationships. The result is a multi-strategy approach in which the analogy brings to a generalization, that is, in turn, a novel description to reason over and over again. An example is provided to show the behavior and effect of the proposed generalization approach.
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Notes
- 1.
This connectionist approach to constraint satisfaction was investigated in [16].
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Leuzzi, F., Ferilli, S. (2016). Generalizing Patterns for Cross-Domain Analogy. In: Ceci, M., Loglisci, C., Manco, G., Masciari, E., Ras, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2015. Lecture Notes in Computer Science(), vol 9607. Springer, Cham. https://doi.org/10.1007/978-3-319-39315-5_10
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