Open AccessProceedings Article
Multiple constraint acquisition
Robin Arcangioli,Christian Bessiere,Nadjib Lazaar +2 more
- 27 Jul 2015
- pp 698-704
TL;DR: A new approach is provided that is able to learn a maximum number of constraints violated by a given negative example and an experimental evaluation shows that this approach improves the state of the art.
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Abstract: QUACQ is a constraint acquisition system that assists a non-expert user to model her problem as a constraint network by classifying (partial) examples as positive or negative. For each negative example, QUACQ focuses onto a constraint of the target network. The drawback is that the user may need to answer a great number of such examples to learn all the constraints. In this paper, we provide a new approach that is able to learn a maximum number of constraints violated by a given negative example. Finally we give an experimental evaluation that shows that our approach improves the state of the art.
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Citations
Efficient Methods for Constraint Acquisition
Dimosthenis C. Tsouros,Kostas Stergiou,Panagiotis Sarigiannidis +2 more
- 27 Aug 2018
TL;DR: This work proposes an algorithm that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as Multi Acq does after a negative example, but with a lower complexity.
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Structure-Driven Multiple Constraint Acquisition
Dimosthenis C. Tsouros,Kostas Stergiou,Christian Bessiere +2 more
- 30 Sep 2019
TL;DR: This paper proposes M QuAcq-2, a new algorithm based on MQuAcq that integrates solutions to both the structure of the learned problem and the number of recursive calls to the main procedure of MQu Acq, making it impractical for large problems.
16
•Posted Content
Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems.
TL;DR: Nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop are presented and evaluated, which range from the choice of visual representation for solutions and constraints to the use of a solution gallery to support exploration of alternate solutions.
14
Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems
TL;DR: In this paper, the authors present and evaluate nine recommendations for the design of interactive visualisation tools supporting the Problem-Solving Loop (PSL) model for vehicle routing problems with time windows.
14
Classifier-based constraint acquisition
TL;DR: This work proposes a known but not widely-used application of machine learning to constraint acquisition: training a classifier to discriminate between solutions and non-solutions, then deriving a constraint model from the trained classifier.
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