Abstract:
Analogy and analogical reasoning is one of the most studied representatives of a family of non-classical forms of reasoning working across different domains.
In the first part of the talk, I will shortly introduce general principles of computational analogy models (relying on a generalization-based approach to analogy-making) and will have a closer look at Heuristic-Driven Theory Projection (HDTP) as an example for a theoretical framework and implemented system. HDTP computes analogical relations and inferences for domains which are presented in many-sorted first-order logic languages, using a restricted form of higher-order anti-unification for finding common generalizations encompassing structurally shared elements common to both domains. The system presentation will be followed by a few reflections on the “cognitive plausibility” of the approach motivated by theoretical complexity and tractability considerations.
The second part of the talk will discuss an application of HDTP to modeling essential parts of concept blending processes as current “hot topic” in Cognitive Science. Here, I will sketch an analogy-inspired account of concept blending—developed in the European FP7-funded Concept Invention Theory (COINVENT) project—combining HDTP with mechanisms from Case-Based Reasoning.