M. Afzal Upal, Dalhousie University, Canada
Considerable work has been done to automatically learn domain-specific knowledge to improve the performance of domain independent problem solving systems. However, most of this work has focussed on learning search control knowledge-knowledge that can be used by a problem solving system during search to improve its performance. An alternative approach to improving the performance of domain independent systems is by using rewriting rules. These are the rules that can be used by a problem solving system after generating an initial plan in order to transform it into a higher quality solution. This paper reviews various approaches that have been suggested for automatically learning rewriting rules, analyses them, and suggests novel algorithms for learning plan rewriting rules.