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
Using plan recognition in human-computer collaboration
UM '99 Proceedings of the seventh international conference on User modeling
Building a Stochastic Dynamic Model of Application Use
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
A decision theoretic approach for interface agent development
A decision theoretic approach for interface agent development
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
From interaction data to plan libraries: a clustering approach
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
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
Interaction analysis for adaptive user interfaces
ICIC'10 Proceedings of the 6th international conference on Advanced intelligent computing theories and applications: intelligent computing
Inferring user search intention based on situation analysis of the physical world
UIC'10 Proceedings of the 7th international conference on Ubiquitous intelligence and computing
Modeling sequences of user actions for statistical goal recognition
User Modeling and User-Adapted Interaction
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A key aspect to study in the field of interface agents is the need to detect as soon as possible the user intentions. User intentions have an important role for an interface agent because they serve as a context to define the way in which the agents can collaborate with users. Intention recognition can be used to infer the user's intentions based on the observation of the tasks the user performs in a software application. In this work, we propose an approach to model the intentions the user can pursue in an application in a semi-automatic way, based on Variable-Order Markov models. We claim that with appropriate training from the user, an interface agent following our approach will be able both to detect the user intention and the most probable sequence of following tasks the user will perform to achieve his/her intention.