research-article

Finding the odd one out: a fractal analogical approach

Authors:

Keith McGreggor,

Ashok GoelAuthors Info & Claims

C&C '11: Proceedings of the 8th ACM conference on Creativity and cognition

Pages 289 - 298

https://doi.org/10.1145/2069618.2069666

Published: 03 November 2011 Publication History

Abstract

The Odd One Out test of intelligence consists of 3x3 matrix reasoning problems organized in 20 levels of difficulty. Addressing problems on this test appears to require integration of multiple cognitive abilities usually associated with creativity, including visual encoding, similarity assessment, pattern detection, and analogical transfer. We describe a novel fractal technique for addressing visual analogy problems on the Odd One Out test. In our technique, the relationship between images is encoded fractally, capturing inherent self-similarity. The technique starts at a high level of resolution, but, if that is not sufficient to resolve ambiguity, it automatically adjusts itself to the right level of resolution for addressing a given problem. Similarly, the technique automatically starts with searching for similarity between simpler relationships, but, if that is not sufficient to resolve ambiguity, it automatically searches for similarity between higher-order relationships. We present preliminary results from applying the fractal technique on a representative subset of the problems from the Odd One Out test.

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Cited By

Andonian AFosco CMonfort MLee AFeris RVondrick COliva A(2020)We Have So Much in Common: Modeling Semantic Relational Set Abstractions in VideosComputer Vision – ECCV 202010.1007/978-3-030-58523-5_2(18-34)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58523-5_2
Prade HRichard G(2014)From Analogical Proportion to Logical Proportions: A SurveyComputational Approaches to Analogical Reasoning: Current Trends10.1007/978-3-642-54516-0_9(217-244)Online publication date: 23-Mar-2014
https://doi.org/10.1007/978-3-642-54516-0_9

Index Terms

Finding the odd one out: a fractal analogical approach
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
  2. Machine learning
    1. Machine learning approaches
      1. Rule learning

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Published In

cover image ACM Conferences

C&C '11: Proceedings of the 8th ACM conference on Creativity and cognition

November 2011

492 pages

ISBN:9781450308205

DOI:10.1145/2069618

General Chair:
Ashok K. Goel
Georgia Institute of Technology, USA
,
Program Chairs:
Fox Harrell
Massachusetts Institute of Technology, USA
,
Brian Magerko
Georgia Institute of Technology, USA
,
Yukari Nagai
Japan Advanced Institute of Science and Technology, Japan
,
Jane Prophet
Goldsmiths College, University of London, UK

Copyright © 2011 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 03 November 2011

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C&C '11

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C&C '11: Creativity and Cognition 2011

November 3 - 6, 2011

Georgia, Atlanta, USA

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Cited By

Andonian AFosco CMonfort MLee AFeris RVondrick COliva A(2020)We Have So Much in Common: Modeling Semantic Relational Set Abstractions in VideosComputer Vision – ECCV 202010.1007/978-3-030-58523-5_2(18-34)Online publication date: 23-Aug-2020
https://dl.acm.org/doi/10.1007/978-3-030-58523-5_2
Prade HRichard G(2014)From Analogical Proportion to Logical Proportions: A SurveyComputational Approaches to Analogical Reasoning: Current Trends10.1007/978-3-642-54516-0_9(217-244)Online publication date: 23-Mar-2014
https://doi.org/10.1007/978-3-642-54516-0_9

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