SLIM: Semi-Lazy Inference Mechanism for Plan Recognition
  Plan Recognition algorithms require to recognize a complete hierarchy
explaining the agent's actions and goals. While the output of such algorithms
is informative to the recognizer, the cost of its calculation is high in
run-time, space, and completeness. Moreover, performing plan recognition online
requires the observing agent to reason about future actions that have not yet
been seen and maintain a set of hypotheses to support all possible options.
This paper presents a new and efficient algorithm for online plan recognition
called SLIM (Semi-Lazy Inference Mechanism). It combines both a bottom-up and
top-down parsing processes, which allow it to commit only to the minimum
necessary actions in real-time, but still provide complete hypotheses post
factum. We show both theoretically and empirically that although the
computational cost of this process is still exponential, there is a significant
improvement in run-time when compared to a state of the art of plan recognition
algorithm.
