TY - GEN
T1 - Partial Domain Search Tree for Constraint-Satisfaction Problems
AU - Sharon, Guni
N1 - Publisher Copyright:
Copyright © 2015, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - The traditional approach for solving Constraint satisfaction Problems (CSPs) is searching the Assignment Space in which each state represents an assignment to some variables. This paper suggests a new search space formalization for CSPs, the Partial Domain Search Tree (PDST). In each PDST node a unique subset of the original domain is considered, values are excluded from the domains in each node to insure that a given set of constraints is satisfied. We provide theoretical analysis of this new approach showing that searching the PDST is beneficial for loosely constrained problems. Experimental results show that this new formalization is a promising direction for future research. In some cases searching the PDST outperforms the traditional approach by an order of magnitude. Furthermore, PDST can enhance Local Search techniques resulting in solutions that violate up to 30% less constraints.
AB - The traditional approach for solving Constraint satisfaction Problems (CSPs) is searching the Assignment Space in which each state represents an assignment to some variables. This paper suggests a new search space formalization for CSPs, the Partial Domain Search Tree (PDST). In each PDST node a unique subset of the original domain is considered, values are excluded from the domains in each node to insure that a given set of constraints is satisfied. We provide theoretical analysis of this new approach showing that searching the PDST is beneficial for loosely constrained problems. Experimental results show that this new formalization is a promising direction for future research. In some cases searching the PDST outperforms the traditional approach by an order of magnitude. Furthermore, PDST can enhance Local Search techniques resulting in solutions that violate up to 30% less constraints.
UR - https://www.scopus.com/pages/publications/85048725483
U2 - 10.1609/socs.v6i1.18370
DO - 10.1609/socs.v6i1.18370
M3 - Conference contribution
AN - SCOPUS:85048725483
T3 - Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
SP - 196
EP - 200
BT - Proceedings of the 8th Annual Symposium on Combinatorial Search, SoCS 2015
A2 - Lelis, Levi
A2 - Stern, Roni
PB - Association for the Advancement of Artificial Intelligence
T2 - 8th Annual Symposium on Combinatorial Search, SoCS 2015
Y2 - 11 June 2015 through 13 June 2015
ER -