Adaptive Probabilistic Networks with Hidden Variables
Machine Learning - Special issue on learning with probabilistic representations
A Language for Construction of Belief Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Conceptual Modeling Approach for Semantics-Driven Enterprise Applications
On the Move to Meaningful Internet Systems, 2002 - DOA/CoopIS/ODBASE 2002 Confederated International Conferences DOA, CoopIS and ODBASE 2002
Using first-order probability logic for the construction of Bayesian networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Unsupervised recognition of ADLs
SETN'10 Proceedings of the 6th Hellenic conference on Artificial Intelligence: theories, models and applications
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As information and communication technologies are becoming an integral part of our homes, the demand for AmI systems with assistive functionality is increasing. A great effort has been spent on designing and building interoperable middleware solutions to be used as the basis for such system. What is called for, though, is a clear direction in the way uncertainty about acquired knowledge is learnt and employed. This paper presents a probabilistic framework for learning dependencies between components within a home environment. In our approach, the uncertainty is maintained in a probabilistic knowledge base which is automatically built from semantic descriptions and observations of device states and events. The knowledge base can be used by smart applications for performing reasoning about the current flow of system events. Furthermore, some preliminary results obtained from real world data are presented.