Early Prediction of Future Behavioral Strategy from Process Traces

Adaptive systems often need to make task-specific decisions about a person from limited prior evidence: a tutor may need to anticipate how a learner will approach a new problem, a game may need to adapt when a player enters a new level, and a human-AI system may need to infer whether a partner will persist with a plan or switch goals. These decisions depend on person-level behavioral tendencies that shape how people solve related tasks, but such tendencies are difficult to infer from standard behavioral evidence. One approach is to use aggregate outcome summaries, such as scores, completion rates, or productivity measures; these summaries are compact and available across tasks, but can collapse distinct behavioral processes into similar outcomes. Another approach is to use process-level traces, which record how behavior unfolds over time; however, process modeling within a single task can entangle stable person-level tendencies with task-specific layout, timing, and affordances. In this work, we study early cross-task behavioral inference: whether partial process traces from multiple source tasks can reveal transferable person-level behavioral structure that predicts strategy in a held-out target task. We introduce Process-Level Latent Variable Models (PLVM), which encode task-specific traces and fuse them into a shared person-level latent representation for cross-task prediction. In PowerWash Simulator, a naturalistic telemetry dataset of real human gameplay, PLVM uses partial traces from two earlier cleaning tasks to predict whether a player will exhibit locally persistent Zone Planner behavior or frequent Zone Hopper behavior in the held-out Fire Station level. Controlled simulations with known latent behavioral types show that cross-task fusion helps when different source tasks reveal complementary dimensions of a shared latent behavioral process. These results suggest that process-level cross-task modeling can support early prediction of target-task behavioral strategy when waiting to observe sufficient target-task behavior is impractical.