Case{based Meta Learning: Sustained Learning supported by a Dynamically Biased Version Space

It is well{recognized that in practical inductive learning systems the search for a concept must be heavily biased. In addition the bias must be dynamic, adapting to the current learning problem. Another important requirement is sustained learning, allowing transfer from known tasks to new ones. Previous work on dynamic bias has not explicitly addressed learning transfer, while previous case{based learning research su ers from a variety of problems. This paper presents a method of Case{Based Meta Learning (CBML), in which the cases are concepts, rather than instances, and retrieved similar concepts are used as a skeletal version space to speed up learning. CBML is independent of the concept representation language. The CBML{Clerk system, which learns repetitive operating system tasks, is presented as a demonstration.