Metareasoning about propagators for constraint satisfaction

Given the breadth of constraint satisfaction problems (CSPs) and the wide variety of CSP solvers, it is often very difficult to determine a priori which solving method is best suited to a problem. This work explores the use of machine learning to predict which solving method will be most effective for a given problem. We use four different problem sets to determine the CSP attributes that can be used to determine which solving method should be applied. After choosing an appropriate set of attributes, we determine how well j48 decision trees can predict which solving method to apply. Furthermore, we take a cost sensitive approach such that problem instances where there is a great difference in runtime between algorithms are emphasized. We also attempt to use information gained on one class of problems to inform decisions about a second class of problems. Finally, we show that the additional costs of deciding which method to apply are outweighed by the time savings compared to applying the same solving method to all problem instances

An Instance of Adaptive Constraint Propagation

. Constraint propagation algorithms vary in the strength of propagation they apply. This paper investigates a simple configuration for adaptive propagation -- the process of varying the strength of propagation to reflect the dynamics of search. We focus on two propagation methods, Arc Consistency (AC) and Forward Checking (FC). AC-based algorithms apply a stronger form of propagation than FC-based algorithms; they invest greater computational effort to detect inconsistent values earlier. The relative payoff of maintaining AC during search as against FC may vary for different constraints and for different intermediate search states. We present a scheme for Adaptive Arc Propagation (AAP) that allows the flexible combination of the two methods. Meta-level reasoning and heuristics are used to dynamically distribute propagation effort between the two. One instance of AAP, Anti-Functional Reduction (AFR), is described in detail here. AFR achieves precisely the same propagation as a pure AC ..