An Algebraic Framework for Solving Proportional and Predictive Analogies

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.

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