Title :
Tools for automating experiment design: a machine learning approach
Author :
Lee, Yongwon ; Clearwater, Scott H.
Author_Institution :
Dept. of Comput. Sci., Pittsburgh Univ., PA, USA
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
Keywords :
intelligent design assistants; knowledge acquisition; learning (artificial intelligence); learning systems; physics computing; HEP-RL; coverage metrics; experiment design; high-energy-physics rule learner; inductive learning tool; machine learning; Artificial intelligence; Calibration; Computer science; Knowledge acquisition; Learning systems; Machine learning; Manuals; Performance analysis; Robustness; Space exploration;
Conference_Titel :
Tools with Artificial Intelligence, 1992. TAI '92, Proceedings., Fourth International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
0-8186-2905-3
DOI :
10.1109/TAI.1992.246423
