Discovering Hierarchical Opponent Strategies via Structural Entropy Minimization in Imperfect-Information Games

In imperfect-information games, an opponent behaves at more than one level, from immediate reactions to strategies that persist over time, but these levels are hidden and the active strategy can change at any time. Most opponent models compress the interaction history into one flat latent state, while methods that use hierarchy require it to be specified in advance or rely on a large language model. We present Structural Entropy-based Opponent Modeling (SEOM), which builds an online interaction graph, finds a minimum-code-length encoding tree by structural entropy minimization, and selects the tree depth from the data. A context-conditional detector compares action distributions within decision contexts, so it responds to genuine strategy changes and stays silent as the learner’s own visits shift; a structural compression ratio, computed before training, indicates whether hierarchy will help. On a benchmark of opponents with planted hierarchies, SEOM recovers every planted depth and partition, with level agreement approaching one as the stream grows, and turns this recovery into gains over flat and structured baselines in return, log-loss, sample efficiency, and post-switch adaptation. The detector identifies genuine switches while suppressing false alarms under learner drift, and the compression ratio chooses between hierarchical and flat modeling with no cross-validation errors.