Tools for automating experiment design: a machine learning approach

Abstract

Work that uses an inductive learning tool, HEP-RL (high-energy-physics rule learner), in the design of a very complex artifact, a high-energy-physics experiment, is reported. The important contribution is the observation that the results of learning provide a more complete and robust design. This is because there were end users of the learning able to suggest constraints beyond the usual simple coverage metrics. This allowed for more confidence in the design.<>