Publication at Faculty of Mathematics and Physics |
2016
Abstract
The overall performance of classical planner depends heavily on the domain model which can be enhanced by adding control knowledge and heuristics. Both of them are known techniques which can boost the search process in exchange for some computational overhead needed for their repeated evaluation.
Our experiments show that the gain from usage of heuristics and control knowledge is evolving throughout the search process and also depends on the type of search algorithm. We demonstrate the idea using the branch-and-bound and iterative deepening search techniques, both implemented in the Picat planning module.