A Reinforcement Learning Approach to Interval Constraint Propagation

When solving systems of nonlinear equations with interval constraint methods, it has often been observed that many calls to contracting operators do not participate actively to the reduction of domains of variables. Attempts to statically select a subset of efficient contracting operators fail to offer reliable performance speed-ups. By embedding the recencyweighted average Reinforcement Learning method into a constraint propagation algorithm to dynamically learn the best operators, we show that it is possible to obtain robust algorithms with reliable performances on a range of sparse problems. Using a simple heuristics to compute initial weights, we also achieve significant performance speed-ups for dense problems.

• Type: Journal
• Year: 2008
• Where: CONSTRAINTS
• Authors: Frédéric Goualard, Christophe Jermann
• Keywords: CONSTRAINTS 2008, Interval Constraint Methods, Performance Speed-ups, Reliable Performances