Claudia Grundke

Learning-based planning systems learn domain-specific knowledge that helps them to solve unseen tasks from the same planning domain. For this purpose they require a diverse set of training instances. A recent proposal for formal specifications of planning domains allows us to exactly characterize which instances are legal for a domain. We automatically generate planning tasks from such formal specifications by means of a translation to answer set programming. We experimentally examine the scalability of the approach and the suitability for learning-based planning, following the setup of the learning track of the International Planning Competition.

Axioms are a feature of the Planning Domain Definition Language PDDL that can be considered as a generalization of database query languages such as DATALOG. The PDDL standard restricts negative occurrences of predicates in axiom bodies to predicates that are directly set by actions and not derived by axioms. In the literature, authors often deviate from this limitation and only require that the set of axioms is stratifiable. We relate the axioms to fixed point logics to show that this aspect does not affect what queries can be expressed by the axioms.

Planning domains are an important notion, e.g. when it comes to restricting the input for generalized planning or learning approaches. However, domains as specified in PDDL cannot fully capture the intuitive understanding of a planning domain. We close this semantic gap and propose using PDDL axioms to characterize the (typically infinite) set of legal tasks of a domain. A minor extension makes it possible to express all properties that can be determined in polynomial time. We demonstrate the suitability of the approach on established domains from the International Planning Competition.