Heuristic Online Goal Recognition in Continuous Domains
This work addresses the problem of real-time goal inference for agents in continuous spaces, such as robotics, but it is incremental as it builds on PRP formulations.
The paper tackled the inefficiency of existing plan recognition by planning (PRP) methods in online goal recognition for continuous domains, resulting in a new algorithm with heuristic decision points that significantly improved run-time in empirical tests over hundreds of experiments.
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.