The other side of the coin: Teaching artificial learning systems

Abstract

The burgeoning technology of machine learning is beginning to provide some insight into the nature of learning and the role of teaching in expediting the learning process. A number of systems that learn concepts and procedures from examples have been described in the research literature. In general these require a teacher who not only has an analytical understanding of the problem domain, but also is familiar with some of the internal workings of the learning system itself. This is because the learner is performing a search in concept space which is generally quite intractable, but for the teacher's selection of guiding examples.

A concept learning system's teacher must select a complete, properly ordered set of examples — one that results in a successful search by the system for an appropriate concept description. In some systems the set of examples determines whether the concept can or cannot be learned, while the order of presentation affects execution time alone. In others, both examples and presentation order are jointly responsible for success. Yet others occasionally select critical examples themselves and present them to a teacher for classification. In all cases, however, the teacher provides the primary means whereby search is pruned. Sometimes the teacher must prime the learner with considerable initial knowledge before learning can begin.

Not surprisingly, systems which demand more of the teacher are able to learn more sophisticated concepts. This paper examines the relationship between teaching requirements and learning power for current concept learning systems. We introduce concept learning by machine with emphasis on the role of the human teacher in rendering practical an otherwise intractable concept search. Machine learning has drawn many lessons from human learning and will continue to do so. In turn it can contribute more formal, if simpler, analyses of concept learning from examples.