Identifying Sources of Intractability in Cognitive Models: An Illustration Using Analogical Structure Mapping

Abstract

Many computational models in cognitive science and artificial intelligence face the problem of computational intractability when assumed to operate for unrestricted input domains. Tractability may be achieved by restricting the input domain, but some degree of generality is typically required to model human-like intelligence. Moreover, it is often non-obvious which restrictions will render a model tractable or not. We present an analytical tool that can be used to identify sources of intractability in a model’s input domain. For our illustration, we use Gentner’s Structure-Mapping Theory of analogy as a running example.