Online Goal Recognition Combining Landmarks and Planning

Goal recognition is the problem of recognizing the goal of an agent based on an incomplete sequence of observations. Recognizing goals with minimal domain knowledge as an agent executes its plan requires efficient algorithms to sift through a large space of hypotheses. In this paper, we develop an online approach to recognize goals in both continuous and discrete domains using a combination of goal mirroring and a generalized notion of landmarks adapted from the planning literature. Extensive experiments demonstrate the approach is more efficient than state-of-the-art online recognition, and substantially more accurate.