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The use of numerical uncertainty representations allows better modeling ofsome aspects of human evidential reasoning. It also makes knowledge acquisitionand system development, test, and modification more difficult. We propose thatwhere possible, the assignment and/or refinement of rule weights should beperformed automatically. We present one approach to performing this training -numerical optimization - and report on the results of some preliminary tests intraining rule bases. We also show that truth maintenance can be used to maketraining more efficient and ask some epistemological questions raised bytraining rule weights.

The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations