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Solve a Constraint Problem without Modeling It

Christian Bessière 1 Remi Coletta 1 Nadjib Lazaar 1
1 COCONUT - Agents, Apprentissage, Contraintes
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : We study how to find a solution to a constraint problem without modeling it. Constraint acquisition systems such as Conacq or ModelSeeker are not able to solve a single instance of a problem because they require positive examples to learn. The recent QuAcq algorithm for constraint acquisition does not require positive examples to learn a constraint network. It is thus able to solve a constraint problem without modeling it: we simply exit from QuAcq as soon as a complete example is classified as positive by the user. In this paper, we propose ASK&SOLVE, an elicitation-based solver that tries to find the best tradeoff between learning and solving to converge as soon as possible on a solution. We propose several strategies to speed-up ASK&SOLVE. Finally we give an experimental evaluation that shows that our approach improves the state of the art.
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Christian Bessière, Remi Coletta, Nadjib Lazaar. Solve a Constraint Problem without Modeling It. ICTAI: International Conference on Tools with Artificial Intelligence, Nov 2014, Limasso, Cyprus. pp.1-7, ⟨10.1109/ICTAI.2014.12⟩. ⟨lirmm-01228368⟩



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