The Automatic Training of Rule Bases that Use Numerical Uncertainty Representations

TL;DR

This paper explores automatic training of rule bases with numerical uncertainty, using optimization and truth maintenance to improve efficiency and address epistemological issues in evidential reasoning systems.

Contribution

It introduces a method for automatic rule weight training using numerical optimization and truth maintenance, enhancing knowledge base development.

Findings

Preliminary tests show promising results in training rule bases.

Truth maintenance improves training efficiency.

Raises epistemological questions about rule weight training.

Abstract

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