Correcting and Extending Domain Knowledge Using Outside Guidance

Abstract

Analytic learning techniques, such as explanation-based learning (EBL), can be powerful methods for acquiring knowledge about a domain where there is a pre-existing theory of the domain. One application of EBL has been to learning apprentice systems where the solution to a problem generated by a human is used as input to the learning process. The learning system analyzes the example and is then able to solve similar problems without outside assistance. One limitation of EBL is that the domain theory must be complete and correct. In this paper we present a general technique for learning from outside guidance that can correct and extend a domain theory. In contrast to hybrid systems that use both analytic and empirical techniques, our approach is completely analytic, using the chunking learning mechanism in the Soar architecture. This technique is demonstrated for a block manipulation task that uses real blocks, a Puma robot arm and a camera vision system.