Heuristic Online Goal Recognition in Continuous Domains
  Goal recognition is the problem of inferring the goal of an agent, based on
its observed actions. An inspiring approach - plan recognition by planning
(PRP) - uses off-the-shelf planners to dynamically generate plans for given
goals, eliminating the need for the traditional plan library. However, existing
PRP formulation is inherently inefficient in online recognition, and cannot be
used with motion planners for continuous spaces. In this paper, we utilize a
different PRP formulation which allows for online goal recognition, and for
application in continuous spaces. We present an online recognition algorithm,
where two heuristic decision points may be used to improve run-time
significantly over existing work. We specify heuristics for continuous domains,
prove guarantees on their use, and empirically evaluate the algorithm over
hundreds of experiments in both a 3D navigational environment and a cooperative
robotic team task.
