Department of Computing,
University of Bradford
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Reasoning under Uncertainty
Research by Dr P. Rhodes and Mr G. Garside
Dr Rhodes, Mr Garside and two research students are doing research
into the use of probability for Reasoning under Uncertainty. Their
aim is to extend the applicability of causal networks to situations in
which the available information is incomplete or not in a suitable
form. This is being achieved by using whatever causal information is
available to find the global maximum entropy solution and hence a
minimally prejudiced estimate of the information required by the
causal network. The global maximum entropy solution, hitherto thought
to be exponentially complex, has been shown to be obtainable by local
computations in linear time for certain classes of causal networks.
For some of these classes, the traditional Lagrangian method for
finding the global maximum entropy solution has had to be extended to
handle non linear constraints arising from independence between
events. Algorithms for computing minimally prejudiced causal
information have been developed using these results. Future research
will attempt to extend these results to all classes of causal network.
When this has been done, it will be possible to produce a pre-processor for
converting incomplete information into a form which can be used by current
packages based on causal networks, eg HUGIN. A longer term aim is to extend
the work beyond the formal structures of causal networks with the intention
of applying it to decision making which, in most practical circumstances, has
to be done with incomplete causal and non causal information.
Author: P.Rhodes
Last update: 22/1/96
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