Title:
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Unsupervised classification of planning instances
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Author:
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Segovia-Aguas, Javier; Jiménez, Sergio; Jonsson, Anders, 1973-
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Abstract:
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Comunicació presentada a: The 27th International Conference on Automated Planning and Scheduling, ICAPS 2017, celebrada a Pittsburgh, Estats Units, del 18 al 23 de juny de 2017 |
Abstract:
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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. |
Abstract:
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This work is partially supported by grant TIN2015-67959 and the Maria de Maeztu Units of Excellence Programme MDM-2015-0502, MEC, Spain. |
Subject(s):
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-Unsupervised learning -Classical planning -Planning programs |
Rights:
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© 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org)
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Document type:
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Conference Object Article - Accepted version |
Published by:
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Association for the Advancement of Artificial Intelligence (AAAI)
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