Skill transfer through goal-driven representation mapping

Abstract

In this paper, we present an approach to transfer that involves analogical mapping of symbols across different domains. We relate this mechanism to I carus, a theory of the human cognitive architecture. Our system can transfer skills across domains hypothesizing maps between representations, improving performance in novel domains. Unlike previous approaches to analogical transfer, our method uses an explanatory analysis that compares how well a new domain theory explains previous solutions under different mapping hypotheses. We present experimental evidence that the new mechanism improves transfer over I carus’ basic learning processes. Moreover, we argue that the same features which distinguish I carus from other architectures support representation mapping in a natural way and operate synergistically with it. These features enable our analogy system to translate a map among concepts into a map between skills, and to support transfer even if two domains are only partially analogous. We also discuss our system’s relation to other work on analogy and outline directions for future research.