Continuous Search in Constraint Programming

Alejandro Arbelaez 1 Youssef Hamadi 2 Michèle Sebag 3, 4
3 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This work presents the concept of Continuous Search (CS), which objective is to allow any user to eventually get their constraint solver achieving a top performance on their problems. Continuous Search comes in two modes: the functioning mode solves the user's problem instances using the current heuristics model; the exploration mode reuses these instances to train and improve the heuristics model through Machine Learning during the computer idle time. Contrasting with previous approaches, Continuous Search thus does not require that the representative instances needed to train a good heuristics model be available beforehand. It achieves lifelong learning, gradually becoming an expert on the user's problem instance distribution. Experimental validation suggests that Continuous Search can design efficient mixed strategies after considering a moderate number of problem instances
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Alejandro Arbelaez, Youssef Hamadi, Michèle Sebag. Continuous Search in Constraint Programming. 22th International Conference on Tools with Artificial Intelligence, Oct 2010, Arras, France. ⟨inria-00515137⟩

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