Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Classification by minimum-message-length inference
ICCI'90 Proceedings of the international conference on Advances in computing and information
Planning and control
Approximating probabilistic inference in Bayesian belief networks is NP-hard
Artificial Intelligence
Abstraction and approximate decision-theoretic planning
Artificial Intelligence
Bayesian Models for Keyhole Plan Recognition in an Adventure Game
User Modeling and User-Adapted Interaction
The BATmobile: towards a Bayesian automated taxi
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Accounting for context in plan recognition, with application to traffic monitoring
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Adaptive web navigation for wireless devices
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
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The automated prediction of a user's interests and requirements is an area of interest to the Artificial Intelligence community. However, current predictive statistical approaches are subject to theoretical and practical limitations which restrict their ability to make useful predictions in domains such as the WWW and computer games that have vast numbers of values for variables of interest. In this paper, we describe an automated abstraction technique which addresses this problem in the context of Dynamic Bayesian Networks. We compare the performance and computational requirements of fine-grained models built with precise variable values with the performance and requirements of a coarse-grained model built with abstracted values. Our results indicate that complex, coarse-grained models offer performance and computational advantages compared to simpler, fine-grained models.