Exploring Instance Generation for Automated Planning
  Many of the core disciplines of artificial intelligence have sets of standard
benchmark problems well known and widely used by the community when developing
new algorithms. Constraint programming and automated planning are examples of
these areas, where the behaviour of a new algorithm is measured by how it
performs on these instances. Typically the efficiency of each solving method
varies not only between problems, but also between instances of the same
problem. Therefore, having a diverse set of instances is crucial to be able to
effectively evaluate a new solving method. Current methods for automatic
generation of instances for Constraint Programming problems start with a
declarative model and search for instances with some desired attributes, such
as hardness or size. We first explore the difficulties of adapting this
approach to generate instances starting from problem specifications written in
PDDL, the de-facto standard language of the automated planning community. We
then propose a new approach where the whole planning problem description is
modelled using Essence, an abstract modelling language that allows expressing
high-level structures without committing to a particular low level
representation in PDDL.
