Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
The active badge location system
ACM Transactions on Information Systems (TOIS)
Real-world applications of Bayesian networks
Communications of the ACM
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Dynamic bayesian networks: representation, inference and learning
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IEEE Wireless Communications
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Personal and Ubiquitous Computing
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MUCS '09 Proceedings of the 6th international workshop on Managing ubiquitous communications and services
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Expert Systems with Applications: An International Journal
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Research in advanced context-aware systems has clearly shown a need to capture the inherent uncertainty in the physical world, especially in human behavior. Modelling approaches that employ the concept of probability, especially in combination with Bayesian methods, are promising candidates to solve the pending problems. This paper analyzes the requirements for such models in order to enable user-friendly, adaptive and especially scalable operation of context-aware systems. It is conjectured that a successful system may not only use Bayesian techniques to infer probabilities from known probability tables but learn, i.e. estimate the probabilities in these tables by observing user behavior.