  Tactical Decision Games (TDGs) are military conflict scenarios presented both
textually and graphically on a map. These scenarios provide a challenging
domain for machine learning because they are open-ended, highly structured, and
typically contain many details of varying relevance. We have developed a
problem-solving component of an interactive companion system that proposes
military tasks to solve TDG scenarios using a combination of analogical
retrieval, mapping, and constraint propagation. We use this problem-solving
component to explore analogical learning.
  In this paper, we describe the problems encountered in learning for this
domain, and the methods we have developed to address these, such as partition
constraints on analogical mapping correspondences and the use of incremental
remapping to improve robustness. We present the results of learning experiments
that show improvement in performance through the simple accumulation of
examples, despite a weak domain theory.
