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This paper discusses the role that background knowledge can play inbuilding flexible multistrategy learning systems. We contend that avariety of learning strategies can be embodied in the backgroundknowledge provided to a general purpose learning algorithm. To beeffective, the general purpose algorithm must have a mechanism forlearning new concept descriptions that can refer to knowledgeprovided by the user or learned during some other task. The method ofknowledge representation is a central problem in designing such asystem since it should be possible to specify background knowledge insuch a way that the learner can apply its knowledge to newinformation.

Using Background Knowledge to Build Multistrategy Learners