Siebers, Michael2018-01-082018-01-0820142014https://dl.gi.de/handle/20.500.12116/11395In this paper we present an approach to avoid dead-ends during automated plan generation. A first-order logic formula can be learned that holds in a state if the application of a specific action will lead to a dead-end. Starting from small problems within a problem domain examples of states where the application of the action will lead to a dead-end will be collected. The states will be generalized using inductive logic programming to a first-order logic formula. We will show how different notions of goal-dependence could be integrated in this approach. The formula learned will be used to speed-up automated plan generation. Furthermore, it provides insight into the planning domain under consideration.Automated planningDead-end statesInductive logic programmingLearning domain knowledgeText/Journal Article1610-1987