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
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