Homogeneous logical proportions: Their uniqueness and their role in similarity-based prediction

Publication Type:
Conference Proceeding
Citation:
13th International Conference on the Principles of Knowledge Representation and Reasoning, KR 2012, 2012, pp. 402 - 412
Issue Date:
2012-12-01
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Given a 4-tuple of Boolean variables (a, b, c, d), logical proportions are modeled by a pair of equivalences relating similarity indicators (a∧b̄ and ā∧b̄), or dissimilarity indicators (a ∧ b̄ and ā ∧ b) pertaining to the pair (a, b), to the ones associated with the pair (c, d). Logical proportions are homogeneous when they are based on equivalences between indicators of the same kind. There are only 4 such homogeneous proportions, which respectively express that i) "a differs from b as c differs from d" (and "b differs from a as d differs from c"), ii) "a differs from b as d differs from c" (and "b differs from a as c differs from d"), iii) "what a and b have in common c and d have it also", iv) "what a and b have in common neither c nor d have it". We prove that each of these proportions is the unique Boolean formula (up to equivalence) that satisfies groups of remarkable properties including a stability property w.r.t. a specific permutation of the terms of the proportion. The first one (i) is shown to be the only one to satisfy the standard postulates of an analogical proportion. The paper also studies how two analogical proportions can be combined into a new one. We then examine how homogeneous proportions can be used for diverse prediction tasks. We particularly focus on the completion of analogical-like series, and on missing value abduction problems. Finally, the paper compares our approach with other existing works on qualitative prediction based on ideas of betweenness, or of matrix abduction. Copyright © 2012, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
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