Publication at Faculty of Mathematics and Physics |
2008
Abstract
There are a lot of approaches for solving planning problems. Many of these approaches are based on 'brute force' search methods and do not care about structures of plans previously computed in certain planning domains.
By analyzing these structures we can obtain useful knowledge that can help in finding solutions for more complex planning problems. The method described in this paper is designed for gathering macrooperators by analyzing of training plans.
This analysis is based on investigation of action dependencies in the training plans. Knowledge gained by our method can be passed directly to planning algorithms to improve their efficiency.