An Algebraic Framework for Solving Proportional and Predictive Analogies
Ute Schmid, Helmar Gust, Kai‐Uwe Kühnberger, Jochen Burghardt · 2019
We present an approach to analogical reasoning which is inherently dependent on abstraction. While typical cognitive and AI models of analogy perform a direct mapping from objects of the base to objects of the target domain, our model performs mapping via abstraction . Abstraction is calculated as most specific generalization of the base and the target structure. In contrast to existing models, learning occurs as a side-effect of analogical reasoning. Our approach is based on the formally sound framework of anti-unification. It allows to deal with different kinds of analogy in a uniform way. After a description of the basic ideas of the approach, we will present examples from the domains of proportional and predictive analogy.