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A Dynamic Approach to Probabilistic Inference
In this paper we present a framework for dynamically constructing Bayesian networks.
What is an Optimal Diagnosis?
We argue that the most appropriate definition of (optimal) diagnosis needs to take into account the utility of outcomes and what the diagnosis is used for.
Can Uncertainty Management be Realized in a Finite Totally Ordered Probability Algebra?
In this paper, the feasibility of using finite totally ordered probability models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is investigated.
Probabilistic Semantics and Defaults
There is much interest in providing probabilistic semantics for defaults but most approaches seem to suffer from one of two problems: either they require numbers, a problem defaults were intended to avoid, or they generate peculiar side effects.
Towards Solving the Multiple Extension Problem: Combining Defaults and Probabilities
Here we suggest a framework that combines probabilities and defaults in a single unified framework that retains the semantics of diagnosis as construction of explanations from a fixed set of possible hypotheses.
