Learning Action Models from Disordered and Noisy Plan Traces
  There is increasing awareness in the planning community that the burden of
specifying complete domain models is too high, which impedes the applicability
of planning technology in many real-world domains. Although there have many
learning systems that help automatically learning domain models, most existing
work assumes that the input traces are completely correct. A more realistic
situation is that the plan traces are disordered and noisy, such as plan traces
described by natural language. In this paper we propose and evaluate an
approach for doing this. Our approach takes as input a set of plan traces with
disordered actions and noise and outputs action models that can best explain
the plan traces. We use a MAX-SAT framework for learning, where the constraints
are derived from the given plan traces. Unlike traditional action models
learners, the states in plan traces can be partially observable and noisy as
well as the actions in plan traces can be disordered and parallel. We
demonstrate the effectiveness of our approach through a systematic empirical
evaluation with both IPC domains and the real-world dataset extracted from
natural language documents.
