My thesis work falls into the areas of AI planning and machine learning. Previous work in these areas focused on making planning efficient and overlooked the need to generate good, production-quality plans. We found this an essential step for transforming planners from research tools into real-world applications. In the thesis I have developed a learning algorithm to automatically acquire control knowledge that guides the planner towards better solutions based on problem solving experience. The planner's domain theory is extended with a plan quality evaluation function. The learner interacts with a human domain expert who suggests improvements to the plans. My system interprets this feedback and automatically operationalizes it into effective planning control knowledge. In the thesis I have fully implemented the learning algorithm, and demonstrated it in several domains including a large process planning domain obtaining significant plan quality improvement. A longer summary is available in Postscript.