Case Base Maintenance in Preference-Based CBR

Abdel-Aziz, Amira; Hüllermeier, Eyke

doi:10.1007/978-3-319-24586-7_1

Cited by 4 publications

(1 citation statement)

References 13 publications

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“…Cases stating preferences are not usual in CBR literature, although they are clearly cases of interest. Still this should not be confused with "preference-based CBR" [2] where preferences between competing solutions in the context of a problem are handled. 1 Preference learning has become a popular artificial intelligence topic [7,9,10,12].…”

Section: Introductionmentioning

confidence: 99%

Predicting Preferences by Means of Analogical Proportions

Bounhas¹,

Pirlot

Prade

2018

Case-Based Reasoning Research and Development

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Open Archive Toulouse Archive OuverteOATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible To cite this version: Bounhas, Myriam and Pirlot, Marc and Prade, Henri Predicting preferences by means of analogical proportions. (Abstract. It is assumed that preferences between two items, described in terms of criteria values belonging to a finite scale, are known for a limited number of pairs of items, which constitutes a case base. The problem is then to predict the preference between the items of a new pair. A new approach based on analogical proportions is presented. Analogical proportions are statements of the form "a is to b as c is to d". If the change between item-1 and item-2 is the same as the change between item-3 and item-4, and a similar statement holds for item'-1, item'-2, item'-3, item'-4, then one may plausibly assume that the preference between item-1 and item'-1 is to the preference between item-2 and item'-2 as the preference between item-3 and item'-3 is to the preference between item-4 and item'-4. This offers a basis for a plausible prediction of the fourth preference if the three others are known. This approach fits well with the postulates underlying weighted averages. Two algorithms are proposed that look for triples of preferences appropriate for a prediction. The first one only exploits the given set of examples. The second one completes this set with new preferences deducible from this set under a monotony assumption. This completion is limited to the generation of preferences that are useful for the requested prediction. The predicted preferences should fit with the assumption that known preferences agree with a unique unknown weighted average. The reported experiments suggest the effectiveness of the proposed approach.

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