Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Plan recognition for intelligent interfaces
Proceedings of the sixth conference on Artificial intelligence applications
A probabilistic analysis of marker-passing techniques for plan-recognition
Proceedings of the seventh conference (1991) on Uncertainty in artificial intelligence
A Bayesian model of plan recognition
Artificial Intelligence
An architecture for Real-Time Reasoning and System Control
IEEE Expert: Intelligent Systems and Their Applications
A message passing algorithm for plan recognition
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Incorporating default inferences into plan recognition
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Models of plans to support communication: an initial report
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Activity Recognition for Dynamic Multi-Agent Teams
ACM Transactions on Intelligent Systems and Technology (TIST)
Forecasting complex group behavior via multiple plan recognition
Frontiers of Computer Science in China
Generating artificial corpora for plan recognition
UM'05 Proceedings of the 10th international conference on User Modeling
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To coordinate with other agents in its environment, an agent needs models of what the other agents are trying to do. When communication is impossible or expensive, this information must be acquired indirectly via plan recognition. Typical approaches to plan recognition start with a specification of the possible plans the other agents may be following, and develop special techniques for discriminating among the possibilities. Perhaps more desirable would be a uniform procedure for mapping plans to general structures supporting inference based on uncertain and incomplete observations. In this paper, we describe a set of methods for converting plans represented in a flexible procedural language to observation models represented as probabilistic belief networks.