Extending analogical generalization with near-misses

Matthew D. McLure, Scott E. Friedman, Kenneth D Forbus

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

Concept learning is a central problem for cognitive systems. Generalization techniques can help organize examples by their commonalities, but comparisons with non-examples, near-misses, can provide discrimination. Early work on near-misses required hand-selected examples by a teacher who understood the learner's internal representations. This paper introduces Analogical Learning by Integrating Generalization and Near-misses (ALIGN) and describes three key advances. First, domain-general cognitive models of analogical processes are used to handle a wider range of examples. Second, ALIGN's analogical generalization process constructs multiple probabilistic representations per concept via clustering, and hence can learn disjunctive concepts. Finally, ALIGN uses unsupervised analogical retrieval to find its own near-miss examples. We show that ALIGN out-performs analogical generalization on two perceptual data sets: (1) hand-drawn sketches; and (2) gcospatial concepts from strategy-game maps.

Original languageEnglish (US)
Title of host publicationProceedings of the 29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
PublisherAI Access Foundation
Pages565-571
Number of pages7
Volume1
ISBN (Electronic)9781577356998
StatePublished - Jun 1 2015
Event29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015 - Austin, United States
Duration: Jan 25 2015Jan 30 2015

Other

Other29th AAAI Conference on Artificial Intelligence, AAAI 2015 and the 27th Innovative Applications of Artificial Intelligence Conference, IAAI 2015
CountryUnited States
CityAustin
Period1/25/151/30/15

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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