Exploration in Machine Learning

Most researchers in machine learning have built their learning systems under the assumption that some external entity would do all the work of furnishing the learning experiences. Recently, however, investigators in several subbelds of machine learning have designed systems that play an active role in choosing the situations from which they will learn. Such activity is generally called exploration. This paper describes a few of these exploratory learning projects, as reported in the literature, and attempts to extract a general account of the issues involved in exploration.