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Analogy as a search procedure : a dimensional view

Osta-Vélez, Matías LU and Gärdenfors, Peter LU (2024) In Journal of Experimental and Theoretical Artificial Intelligence 36(7). p.1135-1154
Abstract
In
this paper, we outline a comprehensive approach to composed analogies
based on the theory of conceptual spaces. Our algorithmic model
understands analogy as a search procedure and builds upon the idea that
analogical similarity depends on a conceptual phenomena called
‘dimensional salience.’ We distinguish between category-based,
property-based, event-based, and part-whole analogies, and propose
computationally-oriented methods for explicating them in terms of
conceptual spaces.In this paper, we outline a comprehensive approach to composed analogies
based on the theory of conceptual spaces. Our algorithmic model
understands analogy as a search procedure and builds upon the idea that ... (More)
In
this paper, we outline a comprehensive approach to composed analogies
based on the theory of conceptual spaces. Our algorithmic model
understands analogy as a search procedure and builds upon the idea that
analogical similarity depends on a conceptual phenomena called
‘dimensional salience.’ We distinguish between category-based,
property-based, event-based, and part-whole analogies, and propose
computationally-oriented methods for explicating them in terms of
conceptual spaces.In this paper, we outline a comprehensive approach to composed analogies
based on the theory of conceptual spaces. Our algorithmic model
understands analogy as a search procedure and builds upon the idea that
analogical similarity depends on a conceptual phenomena called
‘dimensional salience.’ We distinguish between category-based,
property-based, event-based, and part-whole analogies, and propose
computationally-oriented methods for explicating them in terms of
conceptual spaces. (Less)
Please use this url to cite or link to this publication:
author
and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Analogy, categorisation, conceptual spaces, search problems, similarity
in
Journal of Experimental and Theoretical Artificial Intelligence
volume
36
issue
7
pages
20 pages
publisher
Taylor & Francis
external identifiers
  • scopus:85139030683
ISSN
0952-813X
DOI
10.1080/0952813X.2022.2125081
language
English
LU publication?
yes
id
9369c841-fd81-466d-a866-561174958fff
date added to LUP
2022-12-22 08:56:57
date last changed
2024-10-14 11:58:24
@article{9369c841-fd81-466d-a866-561174958fff,
  abstract     = {{In<br>
 this paper, we outline a comprehensive approach to composed analogies <br>
based on the theory of conceptual spaces. Our algorithmic model <br>
understands analogy as a search procedure and builds upon the idea that <br>
analogical similarity depends on a conceptual phenomena called <br>
‘dimensional salience.’ We distinguish between category-based, <br>
property-based, event-based, and part-whole analogies, and propose <br>
computationally-oriented methods for explicating them in terms of <br>
conceptual spaces.In this paper, we outline a comprehensive approach to composed analogies<br>
 based on the theory of conceptual spaces. Our algorithmic model <br>
understands analogy as a search procedure and builds upon the idea that <br>
analogical similarity depends on a conceptual phenomena called <br>
‘dimensional salience.’ We distinguish between category-based, <br>
property-based, event-based, and part-whole analogies, and propose <br>
computationally-oriented methods for explicating them in terms of <br>
conceptual spaces.}},
  author       = {{Osta-Vélez, Matías and Gärdenfors, Peter}},
  issn         = {{0952-813X}},
  keywords     = {{Analogy; categorisation; conceptual spaces; search problems; similarity}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{1135--1154}},
  publisher    = {{Taylor & Francis}},
  series       = {{Journal of Experimental and Theoretical Artificial Intelligence}},
  title        = {{Analogy as a search procedure : a dimensional view}},
  url          = {{http://dx.doi.org/10.1080/0952813X.2022.2125081}},
  doi          = {{10.1080/0952813X.2022.2125081}},
  volume       = {{36}},
  year         = {{2024}},
}