| Emde W. (1989): "An Inference Engine for Representing Multiple Theories", in Knowledge Representation and Organization in Machine Learning, K. Morik (ed.), pp. 148-176, Springer Verlag. |
....criterion currently used in Krt is only a rudimentary answer to this problem. Last, and certainly not least, is the open question of whether there is something to be gained by combining specialization and generalizing revisions into one general and theory revision operation, as argued by [Emd89]. Such an operation would most likely be driven mainly not by criteria such as completeness or correctness, but by scientific notions about the structural qualities of a good theory. Acknowledgements This work has been supported partially by the European Community ESPRIT program in projects MLT ....
Werner Emde. An inference engine for representing multiple theories. In Katharina Morik, editor, Knowledge Representation and Organization in Machine Learning, pages 148--176. Springer Verlag, Berlin, New York, 1989.
....of word meanings from context using a knowledge intensive approach. But our work differs from theirs in that the need to cope with several competing concept hypotheses and to aim at a reason based selection is not an issue in these studies. This is also the major distinction to Emde s work [2] who provides a general logical representation and metareasoning framework for learning tasks. While his approach allows to represent and reason about multiple, competing theories, it does not model the qualitative aspects of competition as we do. 5 CONCLUSION We have introduced a formal approach ....
W. Emde, An inference engine for representing multiple theories', in KnowledgeRepresentation andOrganization in Machine Learning, ed., K. Morik, 148--176, Springer, (1989).
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Emde W. (1989): "An Inference Engine for Representing Multiple Theories", in Knowledge Representation and Organization in Machine Learning, K. Morik (ed.), pp. 148-176, Springer Verlag.
No context found.
Emde W. (1989): "An Inference Engine for Representing Multiple Theories", in Knowledge Representation and Organization in Machine Learning, K. Morik (ed.), pp. 148-176, Springer Verlag.
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