@InProceedings{Schill1991,
  author    = {K. Schill and E. Poeppel and C. Zetzsche},
  title     = {Completing knowledge by competing hierarchies},
  booktitle = {Uncertainty in Artificial Intelligence},
  year      = {1991},
  pages     = {348-352},
  abstract  = {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 selforganizing, 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 choosen by the control stratgey 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 multihierarchical 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).},
}