Analogical Learning in Tactical Decision Games

A longstanding challenge for machine learning is to learn from complex structured examples in broad, open domains. We believe that domain-independent analogical mapping and constraint propagation can form an effective foundation for such learning. Our experience applying these techniques to Tactical Decision Games led us to develop several strategies that make use of limited domain knowledge to assist in the transfer and adaptation of precedents. Although these additional techniques require some domain-specific knowledge, we believe them to be useful in a broad variety of domains. We have been exploring analogical learning as part of developing interactive companion systems (Forbus and Hinrichs, 2004), software agents that learn over the long term. One important aspect of a companion is that it should learn from experience by accumulating examples. This is a weak form of learning that we expect to augment eventually with facilities for generalization, but it is a critical capability nevertheless.