@inProceedings{
 title = {Unsupervised classification of planning instances},
 type = {inProceedings},
 year = {2017},
 identifiers = {[object Object]},
 id = {29267891-1b18-3293-a0e5-ca849303d219},
 created = {2019-03-14T08:15:21.327Z},
 file_attached = {false},
 profile_id = {de5765e4-e253-3166-8178-333c824974ba},
 last_modified = {2019-03-14T08:17:20.111Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {false},
 abstract = {Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). 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.},
 bibtype = {inProceedings},
 author = {Segovia, J. and Jiménez, S. and Jonsson, A.},
 booktitle = {Proceedings International Conference on Automated Planning and Scheduling, ICAPS}
}

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