Abstract
The focus of this paper is on the practical aspects of efficiently resolving inconsistencies when merging probabilistic rule sets. We consider the problem of prioritized merging, when one core knowledge base is to be used without modifications and to be extended by information from other sources. This problem is addressed by our flexible system Heureka that aims at restoring consistency by finding those parts of the additional rule bases which are compatible with the core base and are considered most valuable by the user. We give an overview on the methodological framework of the system and describe some details of its main techniques. In particular, Heureka offers a convenient interface to inductive probabilistic reasoning on maximum entropy. An example from the domain of auditing illustrates the problem and the practical applicability of our framework.
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Finthammer, M., Kern-Isberner, G., Ritterskamp, M. (2007). Resolving Inconsistencies in Probabilistic Knowledge Bases. In: Hertzberg, J., Beetz, M., Englert, R. (eds) KI 2007: Advances in Artificial Intelligence. KI 2007. Lecture Notes in Computer Science(), vol 4667. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74565-5_11
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DOI: https://doi.org/10.1007/978-3-540-74565-5_11
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