TY - GEN
T1 - A choice functions portfolio for solving constraint satisfaction problems
AU - SOTO DE GIORGIS, RICARDO JAVIER
AU - CRAWFORD LABRIN, BRODERICK
AU - Olivares, Rodrigo
PY - 2016/2/23
Y1 - 2016/2/23
N2 - Constraint Programming (CP) allows to solve constraint satisfaction and optimization problems by building and then exploring a search tree of potential solutions. Potential solutions are generated by firstly selecting a variable and then a value from the given problem, phase known as enumeration. In this context, Autonomous Search (AS) that is a particular case of adaptive systems, enables the problem solver to control and adapt its internal configuration during solving time, based on performance metrics in order to be more efficient. The goal is to provide a mechanism for CP solvers, integrating a component able to evaluate the solving performance process. In particular, we employ a classic decision making method called Choice Function (CF). In this paper, we present an evaluation of different choice functions, based on performance exhibited in a indicators set. The results are promising and show that it is feasible to solve constraint satisfaction problems with this new technique.
AB - Constraint Programming (CP) allows to solve constraint satisfaction and optimization problems by building and then exploring a search tree of potential solutions. Potential solutions are generated by firstly selecting a variable and then a value from the given problem, phase known as enumeration. In this context, Autonomous Search (AS) that is a particular case of adaptive systems, enables the problem solver to control and adapt its internal configuration during solving time, based on performance metrics in order to be more efficient. The goal is to provide a mechanism for CP solvers, integrating a component able to evaluate the solving performance process. In particular, we employ a classic decision making method called Choice Function (CF). In this paper, we present an evaluation of different choice functions, based on performance exhibited in a indicators set. The results are promising and show that it is feasible to solve constraint satisfaction problems with this new technique.
KW - autonomous search
KW - choice function
KW - Constraint programming
UR - http://www.scopus.com/inward/record.url?scp=84963784221&partnerID=8YFLogxK
U2 - 10.1109/SCCC.2015.7416568
DO - 10.1109/SCCC.2015.7416568
M3 - Conference contribution
T3 - Proceedings - International Conference of the Chilean Computer Science Society, SCCC
BT - Proceedings - 2015 34th International Conference of the Chilean Computer Science Society, SCCC 2015
PB - IEEE Computer Society
Y2 - 9 November 2015 through 13 November 2015
ER -