Local Predictions for Case-Based Plan Recognition

. This paper presents a novel case-based plan recognition system that interprets observations of plan behavior using a case library of past observations. The system is novel in that it represents a plan as a sequence of actionstate pairs rather than a sequence of actions preceded by some initial state and followed by some final goal state. The system utilizes a unique abstraction scheme to represent indices into the case base. The paper examines and evaluates three different methods for prediction. The first method is prediction without adaptation; the second is predication with adaptation, and the third is prediction with heuristics. We show that the first method is better than a baseline random prediction, that the second method is an improvement over the first, and that the second and the third methods combined are the best overall strategy.