Learning to improve plan quality
Adaptive automated planning systems that can, over time, improve the quality of plans they produce are a promising prospect. The first part of the article discusses the issues involved in designing quality improving learning for planning systems and reviews recent work on learning to improve plan quality. The second part describes our work on the Performance Improving Planning (PIP) System. The heart of PIP is an analytic technique that compares two planning episodes for solving a planning problem that led to two different quality solutions-a higher-quality solution and a lower quality solution-and identifies the critical differences that were responsible for the resulting differences in the quality of the completed plans. We compare the effectiveness of two different ways of storing and applying the knowledge learned from this analysis-as search-control rules and as rewrite rules. The results show that the search-control rules are more effective in improving plan quality. Further analysis of PIP-search-control-the version of PIP that stores the learned knowledge as search-control rules-shows that it is an effective technique for improving plan quality in a variety of situations.
search-control rules rewrite rules machine learning ai planning