Abstract
Reasoning schemes in artificial intelligence (and elsewhere) use information and knowledge, but the inference my depend on assumptions which are uncertain. In this case arguments in favour of and against hypotheses can be derived. These arguments may be weighed by their likelihoods and thereby the credibility and plausibility of different possible hypotheses can be obtained. This is, in a nutshell, the idea to be explored and developed in this article.
Research supported by grant 21-32660.91 of the Swiss National Foundation for Research and the Swiss Federal Office for Science and Education, Esprit Basic Research Project Drums II (Defeasible Reasoning and Uncertainty Management Systems).
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Kohlas, J. (1995). Mathematical Foundations of Evidence Theory. In: Coletti, G., Dubois, D., Scozzafava, R. (eds) Mathematical Models for Handling Partial Knowledge in Artificial Intelligence. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-1424-8_3
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DOI: https://doi.org/10.1007/978-1-4899-1424-8_3
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