Efficient planning through automatic configuration and machine learning

Although in the last decade the performance of domain-independent planners has significantly improved, there is no planner that outperforms all the others in every benchmark domains. In many domains, the planning performance can be improved by automatically deriving and exploiting knowledge about the domain and problem structure that is not explicitly given in the input formalization, and that can be used for optimizing the planner behavior. This thesis proposes three innovative techniques for deriving and using additional domain and problem knowledge through automatic algorithm configuration and machine learning techniques: a prediction model of the planning horizons, a method for synthesizing domain-optimized planners from a highly parameterized generic planner, and a system for the automatic configuration of a portfolio planner.