Segovia-Aguas, J, Jiménez, S, Jonsson, A. Unsupervised Classification of Planning Instances. Proceedings of the 27th International Conference on Automated Planning and Scheduling (ICAPS'17)

In this paper we introduce a novel approach for unsupervised classification of planning instances based on the recent formalism of planning programs. Our approach is inspired by structured prediction in machine learning, which aims at predicting structured information about a given input rather than a scalar value. In our case, each input is an unlabelled classical planning instance, and the associated structured information is the planning program that solves the instance. We describe a method that takes as input a set of planning instances and outputs a set of planning programs, classifying each instance according to the program that solves it. Our results show that automated planning can be successfully used to solve structured unsupervised classification tasks, and invites further exploration of the connection between automated planning and structured prediction.

Additional material:

• Post-print at UPF e-repository
• Final version in Open Acces here

• The source code and benchmarks are available at

  https://github.com/aig-upf/automated-programming-framework