  A control strategy for expert systems is presented which is based on Shafer's
Belief theory and the combination rule of Dempster. In contrast to well known
strategies it is not sequentially and hypotheses-driven, but parallel and self
organizing, determined by the concept of information gain. The information
gain, calculated as the maximal difference between the actual evidence
distribution in the knowledge base and the potential evidence determines each
consultation step. Hierarchically structured knowledge is an important
representation form and experts even use several hierarchies in parallel for
constituting their knowledge. Hence the control strategy is applied to a
layered set of distinct hierarchies. Depending on the actual data one of these
hierarchies is chosen by the control strategy for the next step in the
reasoning process. Provided the actual data are well matched to the structure
of one hierarchy, this hierarchy remains selected for a longer consultation
time. If no good match can be achieved, a switch from the actual hierarchy to a
competing one will result, very similar to the phenomenon of restructuring in
problem solving tasks. Up to now the control strategy is restricted to multi
hierarchical knowledge bases with disjunct hierarchies. It is implemented in
the expert system IBIG (inference by information gain), being presently applied
to acquired speech disorders (aphasia).
