Abstract: Many researchers in Artificial Intelligence seek for new algorithms to reduce the amount of memory/ time consumed for general searches in Constraint Satisfaction Problems. These improvements are accomplished by the use of heuristics which either prune useless tree search branches or even indicate the path to reach the (optimal) solution faster than the blind version of the search. Many heuristics were proposed in the literature, like the Least Constraining Value (LCV). In this paper we propose a new pre-processing search heuristic to reduce the amount of backtracking calls, namely the Least Suggested Value First: a solution whenever the LCV solely cannot measure how much a value is constrained. In this paper, we present a pedagogical example, as well as the preliminary results.

Cleyton Rodrigues, Ryan Ribeiro de Azevedo, Fred Freitas and Eric Dantas, “LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem” International Journal of Advanced Research in Artificial Intelligence(IJARAI), 1(9), 2012. http://dx.doi.org/10.14569/IJARAI.2012.010904

@article{Rodrigues2012,
title = {LSVF: a New Search Heuristic to Reduce the Backtracking Calls for Solving Constraint Satisfaction Problem},
journal = {International Journal of Advanced Research in Artificial Intelligence},
doi = {10.14569/IJARAI.2012.010904},
url = {http://dx.doi.org/10.14569/IJARAI.2012.010904},
year = {2012},
publisher = {The Science and Information Organization},
volume = {1},
number = {9},
author = {Cleyton Rodrigues and Ryan Ribeiro de Azevedo and Fred Freitas and Eric Dantas}
}