Combining active learning with inductive logic
programming to close the loop in machine learning

Bryant, CH; Muggleton, SH; Page, CD; Sternberg, MJE

Abstract

Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, it Closed Loop Machine Learning. This is related to the area of Active Learning in which the ML system actively selects experiments to discriminate between contending hypotheses. In Closed Loop Machine Learning the system not only selects but also carries out the experiments in the learning domain. ASE-Progol, a Closed Loop Machine Learning system, is proposed. ASE-Progol will use the ILP system Progol to form the initial hypothesis set. It will then devise experiments to select between competing hypotheses, direct a robot to perform the experiments, and finally analyse the experimental results. ASE-Progol will then revise its hypotheses and repeat the cycle until a unique hypothesis remains. This will be, to our knowledge, the first attempt to use a robot to carry out experiments selected by Active Learning within a real world application.

Citation

programming to close the loop in machine learning. In S. Colton (Ed.), Proceedings of AISB'99 Symposium on AI and Scientific Creativity (59-64). The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB)

• Start Date: Mar 1, 1999
• Publication Date: Jan 1, 1999
• Deposit Date: Feb 17, 2009
• Pages: 59-64
• Book Title: Proceedings of AISB'99 Symposium on AI and Scientific Creativity
• ISBN: 1902956044
• Keywords: active learning, machine learning