Uncertainty Based Selection of Learning Experiences

The training experiences needed by a learning system may be selected by either an external agent or the system itself. We show that knowledge of the current state of the learner's representation, which is not available to an external agent, is necessary for selection of informative experiences. Hence it is advantageous if a learning system can select its own experiences. We show that the uncertainty of the current representation can be used as a heuristic to guide selection of experiences, and describe results obtained with DIDO, an inductive learning system we have developed using an uncertainty based selection heuristic. STRATEGIES FOR SELECTING EXPERIENCES It has long been recognized that the speed with which a system learns is strongly influenced by the particular selection of experiences, from the space of all possible experiences, that the system receives. For example, Winston (1975) showed that 'near misses' (training examples that lie just outside the boundaries...

• Type: Conference
• Year: 1989
• Where: ICML
• Authors: Paul D. Scott, Shaul Markovitch
• Keywords: External Agent, ICML 1989, Informative Experiences, Machine Learning, Selection