A Bayesian model of plan recognition
Artificial Intelligence
Communications of the ACM
Coaching a simulated soccer team by opponent model recognition
Proceedings of the fifth international conference on Autonomous agents
It knows what you're going to do: adding anticipation to a Quakebot
Proceedings of the fifth international conference on Autonomous agents
Evolving rule-based models: a tool for design of flexible adaptive systems
Evolving rule-based models: a tool for design of flexible adaptive systems
Applying Plan Recognition Algorithms To Program Understanding
Automated Software Engineering
Techniques for Plan Recognition
User Modeling and User-Adapted Interaction
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Opponent Modeling in Multi-Agent Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scientific Data Classification: A Case Study
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Intrusion Detection: A Bioinformatics Approach
ACSAC '03 Proceedings of the 19th Annual Computer Security Applications Conference
Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Incorporating observer biases in keyhole plan recognition (efficiently!)
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Goal recognition through goal graph analysis
Journal of Artificial Intelligence Research
RESC: an approach for real-time, dynamic agent tracking
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The use of hidden semi-Markov models in clinical diagnosis maze tasks
Intelligent Data Analysis
A new model of plan recognition
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A methodological approach to mining and simulating data in complex information systems
Intelligent Data Analysis
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To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents or robots, is known as plan recognition, or agent modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge. We present an approach for automatically creating the model of an agent behavior based on the observation and analysis of its atomic behaviors. In this approach, observations of an agent behavior are transformed into a sequence of atomic behaviors (events). This stream is analyzed in order to get the corresponding behavior model, represented by a distribution of relevant events. Once the model has been created, the proposed approach presents a method using a statistical test for classifying an observed behavior. Therefore, in this research, the problem of behavior classification is examined as a problem of learning to characterize the behavior of an agent in terms of sequences of atomic behaviors. The experiment results of this paper show that a system based on our approach can efficiently recognize different behaviors in different domains, in particular UNIX command-line data, and RoboCup soccer simulation.