Analogy as a search procedure : a dimensional view
(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:
https://lup.lub.lu.se/record/9369c841-fd81-466d-a866-561174958fff
- author
- Osta-Vélez, Matías LU and Gärdenfors, Peter LU
- organization
- publishing date
- 2024
- 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}}, }