Search

For Bayesian networks, the MPE problem is the problem of finding a configuration of all unobserved variables such that this configuration has the highest posterior probability given the evidence. In this paper, we aim to gain more insight in the solution space of the MP E problem when evidence or a parameter value is changed and the previous MP E solution is known. Gaining more insight is a requisite for developing better algorithms for solving the problem. We do so by introducing a new representation of the probabilities of configurations in the Bayesian network.

For Bayesian networks, the MPE problem is the problem of finding a configuration of all unobserved variables such that this configuration has the highest posterior probability given the evidence. In this paper, we aim to gain more insight in the solution space of the MP E problem when evidence or a parameter value is changed and the previous MP E solution is known. Gaining more insight is a requisite for developing better algorithms for solving the problem. We do so by introducing a new representation of the probabilities of configurations in the Bayesian network.

It is widely accepted that to extend the effectiveness of information disclosure beyond the limitations of current empirically-based retrieval systems, some notion of document semantics has to be incorporated into the retrieval mechanism. A recent approach to bringing semantics into play is to found the retrieval mechanism on the notion of logical inference. In this paper, we build on this approach and describe a promising new mechanism for information disclosure, called the Refinement Machine. The Refinement Machine features the language of index expressions as a language for characterizing information objects and a deduction mechanism driven by rules of inference. Two types of inference rule are distinguished. The rules of strict inference follow the line of traditional logical deduction. As the characterizations of objects are incomplete and requests are typically partial descriptions of the information need, the rules of strict inference are supplemented with a rule of plausible inference. This rule of plausible inference is motivated by recent work in the area of plausible reasoning in knowledge-based systems and, in particular, is derived from the work on belief networks. Besides giving details of the Refinement Machine, this paper also presents some preliminary experimental results.

Plausible inference is an essential aspect of logic-based information disclosure. This paper proposes a context-sensitive plausible inference mechanism based on a so-called index expression belief network. Plausible inference is cloaked as probabilistic evidence propagation within this network. Preliminary experiments show general evidence propagation algorithms to be too inefficient for real-life information disclosure applications. The paper sketches two optimizations whereby efficient, special-purpose evidence propagation may be realized.