Some recent advances in reasoning based on analogical proportions
Myriam Bounhas, Henri Prade, Gilles Richard

TL;DR
This paper explores recent improvements in reasoning using analogical proportions, focusing on enhancing inference accuracy, computational efficiency, and their potential for explanation, while revealing connections to multi-valued dependencies.
Contribution
It introduces novel methods to improve analogical inference accuracy and efficiency, and uncovers new links between analogical proportions and multi-valued dependencies.
Findings
Enhanced methods for analogical inference accuracy
Reduced computational costs in analogical reasoning
Identified relationships between analogical proportions and multi-valued dependencies
Abstract
Analogical proportions compare pairs of items (a, b) and (c, d) in terms of their differences and similarities. They play a key role in the formalization of analogical inference. The paper first discusses how to improve analogical inference in terms of accuracy and in terms of computational cost. Then it indicates the potential of analogical proportions for explanation. Finally, it highlights the close relationship between analogical proportions and multi-valued dependencies, which reveals an unsuspected aspect of the former.
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Taxonomy
TopicsBayesian Modeling and Causal Inference · Multi-Criteria Decision Making
