RADAR: Finding analogies using attributes of structure

Brian P. Crean, Diarmuid O'Donoghue

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Citations (Scopus)

Abstract

RADAR is a model of analogy retrieval that employs the principle of systematicity as its primary retrieval cue. RADAR was created to address the current bias toward semantics in analogical retrieval models, to the detriment of structural factors. RADAR recalls 100% of structurally identical domains. We describe a technique based on "derived attributes" that captures structural descriptions of the domain's representation rather than domain contents. We detail their use, recall and performance within RADAR through empirical evidence. We contrast RADAR with existing models of analogy retrieval. We also demonstrate that RADAR can retrieve both semantically related and semantically unrelated domains, even without a complete target description, which plagues current models.

Original languageEnglish
Title of host publicationArtificial Intelligence and Cognitive Science - 13th Irish Conference, AICS 2002, Proceedings
EditorsMichael O’Neill, Richard F. E. Sutcliffe, Conor Ryan, Malachy Eaton, Niall J. L. Griffith
PublisherSpringer Verlag
Pages20-27
Number of pages8
ISBN (Electronic)3540441840, 9783540441847
DOIs
Publication statusPublished - 2002
Event13th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2002 - Limerick, Ireland
Duration: 12 Sep 200213 Sep 2002

Publication series

NameLecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume2464
ISSN (Print)0302-9743

Conference

Conference13th Irish Conference on Artificial Intelligence and Cognitive Science, AICS 2002
Country/TerritoryIreland
CityLimerick
Period12/09/0213/09/02

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