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    RADAR: Finding Analogies using Attributes of Structure


    Crean, Brian P. and O'Donoghue, Diarmuid (2002) RADAR: Finding Analogies using Attributes of Structure. In: Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science (LNCS) (2464). Springer, pp. 20-27. ISBN 3540441840

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    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.

    Item Type: Book Section
    Additional Information: Crean B.P., O’Donoghue D. (2002) RADAR: Finding Analogies Using Attributes of Structure. In: O’Neill M., Sutcliffe R.F.E., Ryan C., Eaton M., Griffith N.J.L. (eds) Artificial Intelligence and Cognitive Science. AICS 2002. Lecture Notes in Computer Science, vol 2464. Springer, Berlin, Heidelberg
    Keywords: RADAR; Analogies; Attributes; Structure; Analogy retrieval; systematicity;
    Academic Unit: Faculty of Science and Engineering > Computer Science
    Item ID: 8174
    Identification Number: https://doi.org/10.1007/3-540-45750-X_3
    Depositing User: Dr. Diarmuid O'Donoghue
    Date Deposited: 25 Apr 2017 16:36
    Publisher: Springer
    Refereed: Yes
    URI:

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