An active approach to automatic case generation

Abstract

When learning by observing an expert, cases can be automatically generated in an inexpensive manner. However, since this is a passive method of learning the observer has no control over which problems are solved and this can result in case bases that do not contain a representative distribution of the problem space. In order to overcome this we present a method to incorporate active learning with learning by observation. Problems that are not covered by the current case base are automatically detected, during runtime or by examining secondary case bases, and presented to an expert to be solved. However, we show that these problems can not be presented to the expert individually but need to be part of a sequence of problems. Creating this sequence of cases is non-trivial, and an approach to creating such sequences is described. Experimental results, in the domain of simulated soccer, show our approach to be useful not only for increasing the problem coverage of the case base but also in creating cases with rare solutions. © 2009 Springer Berlin Heidelberg.