Computational models of analogy.

By Dedre Gentner, Kenneth D. Forbus. 2011

243 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cultural Studies, Developmental and Educational Psychology

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Citations in the corpus

Towards Deep Symbolic Reinforcement Learning (2016, methodology): “Second, to further amplify the system’s ability to determine similarity between current and past situations, formal techniques for analogical reasoning can be deployed, such as the structure mapping engine or one of its relatives [36].”
Commonsense reasoning and commonsense knowledge in artificial intelligence (2015, methodology): “Likewise reasoning by analogy (Hofstadter & Sander, 2013) (Gentner & Forbus, 2011) and case-based reasoning (Kolodner, 1993) have been much more extensively studied in informal frameworks than in mathematically grounded frameworks.”
Teaching Mathematics by Comparison: Analog Visibility as a Double-Edged Sword. (2016, background): “Misconceptions are mostly unexplored in prominent structure-mapping models (Gentner, 1983; Gentner & Forbus, 2011).”
Analogical Learning and Reasoning (2013, methodology): “Mapping is the core process of analogy, and it has been the central focus of much analogy research in both psychology and computer science (Gentner & Forbus, 2010).”
The Oxford Handbook of Cognitive Psychology (2013, methodology): “…mapping are also incorporated into other current simulations of analogy, for example, ACME (Holyoak & Thagard, 1989), AMBR (Kokinov & Petrov, 2001), CAB (Larkey & Love, 2003), DORA (Doumas, Hummel, & Sandhofer, 2008), and LISA (Hummel & Holyoak, 1997; see Gentner & Forbus, 2010, for a review).”
A review of verbal and non-verbal human-robot interactive communication (2014, background): “…is a robot with a deeper understanding of the meaning of words referring to abstract concepts, although related work on computational analogy making (such as [67]), could prove to provide some starting points for extensions towards such domains, we are still beyond the current state-of-the-art.”