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While recent work on online goal recognition efficiently infers goals underlow observability, comparatively less work focuses on online goal recognitionthat works in both discrete and continuous domains. Online goal recognitionapproaches often rely on repeated calls to the planner at each new observation,incurring high computational costs. Recognizing goals online in continuousspace quickly and reliably is critical for any trajectory planning problemsince the real physical world is fast-moving, e.g. robot applications. Wedevelop an efficient method for goal recognition that relies either on a singlecall to the planner for each possible goal in discrete domains or a simplifiedmotion model that reduces the computational burden in continuous ones. Theresulting approach performs the online component of recognition orders ofmagnitude faster than the current state of the art, making it the first onlinemethod effectively usable for robotics applications that require sub-secondrecognition.

Online Goal Recognition in Discrete and Continuous Domains Using a Vectorial Representation