Learning during search

Abstract : Autonomous Search is a new emerging area in Constraint Programming, motivated by the demonstrated importance of the application of Machine Learning techniques to the Algorithm Selection Problem, and with potential applications ranging from planning and configuring to scheduling. This area aims at developing automatic tools to improve the performance of search algorithms to solve combinatorial problems, e.g., selecting the best parameter settings for a constraint solver to solve a particular problem instance. In this thesis, we study three different points of view to automatically solve combinatorial problems; in particular Constraint Satisfaction, Constraint Optimization, and SAT problems.First, we present domFD, a new Variable Selection Heuristic whose objective is to heuristically compute a simplified form of functional dependencies called weak dependencies. These weak dependencies are then used to guide the search at each decision point. Second, we study the Algorithm Selection Problem from two different angles. On the one hand, we review a traditional portfolio algorithm to learn offline a heuristics model for the Protein Structure Prediction Problem. On the other hand, we present the Continuous Search paradigm, whose objective is to allow any user to eventually get his constraint solver to achieve 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 training and improve the heuristics model through Machine Learning during the computer idle time. Finally, the last part of the thesis, considers the question of adding a knowledge-sharing layer to current portfolio-based parallel local search solvers for SAT. We show that by sharing the best configuration of each algorithm in the parallel portfolio on regular basis and aggregating this information in special ways, the overall performance can be greatly improved.
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Alejandro Arbelaez Rodriguez. Learning during search. Other [cs.OH]. Université Paris Sud - Paris XI, 2011. English. ⟨NNT : 2011PA112063⟩. ⟨tel-00600523⟩

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