Accounting for Context in Plan Recognition, with Application to Traffic
  Monitoring
  Typical approaches to plan recognition start from a representation of an
agent's possible plans, and reason evidentially from observations of the
agent's actions to assess the plausibility of the various candidates. A more
expansive view of the task (consistent with some prior work) accounts for the
context in which the plan was generated, the mental state and planning process
of the agent, and consequences of the agent's actions in the world. We present
a general Bayesian framework encompassing this view, and focus on how context
can be exploited in plan recognition. We demonstrate the approach on a problem
in traffic monitoring, where the objective is to induce the plan of the driver
from observation of vehicle movements. Starting from a model of how the driver
generates plans, we show how the highway context can appropriately influence
the recognizer's interpretation of observed driver behavior.
