A formal theory of plan recognition and its implementation
Reasoning about plans
A Bayesian model of plan recognition
Artificial Intelligence
Agents that reduce work and information overload
Communications of the ACM
The power of amnesia: learning probabilistic automata with variable memory length
Machine Learning - Special issue on COLT '94
From Interaction Data to Plan Libraries: A Clustering Approach
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Building a Stochastic Dynamic Model of Application Use
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Layered Representations for Human Activity Recognition
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Scalable and adaptive goal recognition
Scalable and adaptive goal recognition
Inferring Activities from Interactions with Objects
IEEE Pervasive Computing
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 03
Learning and inferring transportation routines
Artificial Intelligence
A general model for online probabilistic plan recognition
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The role of intention recognition in the evolution of cooperative behavior
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Corpus-based intention recognition in cooperation dilemmas
Artificial Life
State-of-the-art of intention recognition and its use in decision making
AI Communications
Intelligent Decision Technologies
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The recognition of the goal a user is pursing when interacting with a software application is a crucial task for an interface agent as it serves as a context for making opportune interventions to provide assistance to the user. The prediction of the user goal must be fast and a goal recognizer must be able to make early predictions with few observations of the user actions. In this work we propose an approach to automatically build an intention model from a plan corpus using Variable Order Markov models. We claim that following our approach, an interface agent will be capable of accurately ranking the most probable user goals in a time linear to the number of goals modeled.