Learning Bayesian network structure from large-scale data sets, without any expertspecified ordering of variables, remains a difficult problem. We propose systematic improvements ...
We propose a generic, domain-independent local search method called adaptive search for solving Constraint Satisfaction Problems (CSP). We design a new heuristics that takes advan...
The Quantified Constraint Satisfaction Problem (QCSP) has been introduced to express situations in which we are not able to control the value of some of the variables (the universa...
Abstract. In this work we study an over-constrained scheduling problem where constraints cannot be relaxed. This problem originates from a local defense agency where activities to ...
Andrew Lim, Brian Rodrigues, Ramesh Thangarajo, Fe...
The basic idea to defend in this paper is that an adequate perception of the search space, sacrificing most of the precision, can paradoxically accelerate the discovery of the mo...