Analogical proportion-based classification methods have been introduced a few years ago. They look in the training set for suitable triples of examples that are in an analogical proportion with the item to be classified, on a maximal set of attributes. This can be viewed as a lazy classification technique since, like k-nn algorithms, there is no static model built from the set of examples. The ...

Integration of abstractly similar relations during analogical reasoning was investigated using functional magnetic resonance imaging. Activation elicited by an analogical reasoning task that required both complex working memory and integration of abstractly similar relations was compared to activation elicited by a non-analogical task that required complex working memory in the absence of abstr...

Analogical mapping is a domain-general cognitive process found in language development, and more particularly in the abstraction of construction schemas. Analogical mapping is considered as the general cognitive process which consists in the alignment of two or several sequences in order to detect their common relational structure and generalize it to new items. The current study investigated a...

Using Analogy to Overcome Brittleness in AI Systems Matthew Evans Klenk One of the most important aspects of human reasoning is our ability to robustly adapt to new situations, tasks, and domains. Current AI systems exhibit brittleness when faced with new situations and domains. This work explores how structure mapping models of analogical processing allow for the robust reuse of domain knowled...

This paper describes our development of analogical abduction as an extension to our work on meta level abductive reasoning for rule abduction and predicate invention. Previously, we gave a set of axioms to state the object level causalities in terms of first-order-logic (FOL) clauses, which represent direct and indirect causalities with transitive rules. Here we extend our formalism of the meta...

We present Explanatory Reasoning for Inductive Confidence (ERIC), a computational model of explanation generation and evaluation. ERIC combines analogical hypothesis generation and justification with normative probabilistic theory over statement confidences. It successfully captures a broad range of empirical phenomena, and represents a promising approach toward the application of explanatory k...

This paper presents a novel approach to the prediction of null values in relational databases, based on the notion of analogical proportion. We show in particular how an algorithm initially proposed in a classification context can be adapted to this purpose. This work focuses on the case of a transactional database, where attributes are Boolean. The experimental results reported here, even thou...