Personalized Diagnosis for Over-Constrained Problems
Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence. Pages 1990-1995.
Abstract
Constraint-based applications such as configurators, recommenders, and scheduling systems support users in complex decision making scenarios. Typically, these systems try to identify a solution that satisfies all articulated user requirements. If the requirements are inconsistent with the underlying constraint set, users have to be actively supported in finding a way out from the no solution could be found dilemma. In this paper we introduce techniques that support the calculation of personalized diagnoses for inconsistent constraint sets. These techniques significantly improve the diagnosis prediction quality compared to approaches based on the calculation of minimal cardinality diagnoses. In order to show the applicability of our approach we present the results of an empirical study and a corresponding performance analysis.
@inproceedings{ijcai2013p294,
title = {Personalized Diagnosis for Over-Constrained Problems},
author = {Felfernig, Alexander and Schubert, Monika and Reiterer, Stefan},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {1990--1995},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/294},
}
TY - CPAPER TI - Personalized Diagnosis for Over-Constrained Problems AU - Felfernig, Alexander AU - Schubert, Monika AU - Reiterer, Stefan T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 1990 EP - 1995 UR - https://ijcai.org/proceedings/2013/294 ER -Copied
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