Sequence-Aware Reconstruction for Learning Action Models from Noisy Action-Only Traces

Learning symbolic action models from action-only traces is challenging when action identities, argument objects, and object types are uncertain and no states are observed. We introduce Plan-HMM, which uses a fixed-emission, type-specific hidden Markov model to reconstruct object-event traces and aligned pairwise evidence from weighted action candidates. These outputs support symbolic induction without requiring a complete reconstructed plan. Across nine planning domains, Plan-HMM improves transition recovery and invalid-plan rejection over selecting the highest-weight action candidate at each step, with slightly lower valid-plan acceptance on jointly completed settings. It also completes more settings under a fixed time limit. Additional experiments show improved performance under missing observations represented by candidate completions from a fixed training vocabulary.