Pruning bad quality causal links in sequential satisfying planning

Although current sequential satisficing planners are able to find solutions for a wide range of problems, the generation of good quality plans still remains a challenge. Anytime plan-ners, which use the cost of the last plan found to prune the next search episodes, have shown useful to improve the qual-ity of the solutions. With this in mind this paper proposes a method that exploits the solutions found by an anytime planner to improve the quality of the subsequent ones. The method extracts a set of causal links from the first plans, the plans with worse quality, and creates a more constrained def-inition of the planning task that rejects the creation of these causal links. The performance of the proposed method is eval-uated in domains in which optimization is particularly chal-lenging