Probabilistic State-Dependent Grammars for Plan Recognition
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Journal of Artificial Intelligence Research
Policy recognition in the abstract hidden Markov model
Journal of Artificial Intelligence Research
Generating artificial corpora for plan recognition
UM'05 Proceedings of the 10th international conference on User Modeling
Case-Based Reasoning in Transfer Learning
ICCBR '09 Proceedings of the 8th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Activity recognition with intended actions
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Recognising Agent Behaviour During Variable Length Activities
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Adaptive feedback selection for intelligent tutoring systems
Expert Systems with Applications: An International Journal
Domain Independent Goal Recognition
Proceedings of the 2010 conference on STAIRS 2010: Proceedings of the Fifth Starting AI Researchers' Symposium
Generating corpora of activities of daily living and towards measuring the corpora's complexity
CAVE'12 Proceedings of the First international conference on Cognitive Agents for Virtual Environments
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We present our work on using statistical, corpus-based machine learning techniques to simultaneously recognize an agent's current goal schemas at various levels of a hierarchical plan. Our recognizer is based on a novel type of graphical model, a Cascading Hidden Markov Model, which allows the algorithm to do exact inference and make predictions at each level of the hierarchy in time quadratic to the number of possible goal schemas. We also report results of our recognizer's performance on a plan corpus.