A formal theory of plan recognition and its implementation
Reasoning about plans
Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
Planning and Learning by Analogical Reasoning
Planning and Learning by Analogical Reasoning
On the Role of Abstraction in Case-Based Reasoning
EWCBR '96 Proceedings of the Third European Workshop on Advances in Case-Based Reasoning
Incremental Case-Based Plan Recognition Using State Indices
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
PRODIGY 4.0: The Manual and Tutorial
PRODIGY 4.0: The Manual and Tutorial
Building and refining abstract planning cases by change of representation language
Journal of Artificial Intelligence Research
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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 action-state 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.