Transfer Learning through Analogy in Games

  • Thomas Hinrichs Northwestern University
  • Kenneth D. Forbus Northwestern University


We report on a series of transfer learning experiments in game domains, in which we use structural analogy from one learned game to speed learning of another related game. We find that a major benefit of analogy is that it reduces the extent to which the source domain must be generalized before transfer. We describe two techniques in particular, minimal ascension and metamapping, that enable analogies to be drawn even when comparing descriptions using different relational vocabularies. Evidence for the effectiveness of these techniques is provided by a large-scale external evaluation, involving a substantial number of novel distant analogs.
How to Cite
Hinrichs, T., & Forbus, K. D. (2011). Transfer Learning through Analogy in Games. AI Magazine, 32(1), 70.