TY - GEN
T1 - Efficient recognition of acyclic clustered constraint satisfaction problems
AU - Razgon, Igor
AU - O'Sullivan, Barry
PY - 2007/1/1
Y1 - 2007/1/1
N2 - In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to being acyclic. Such graphs are encountered in many real world application domains such as configuration, diagnosis, model-based reasoning and scheduling. We present a class of variable ordering heuristics that exploit the clustered structure of the constraint network to inform search. We show how these heuristics can be used in conjunction with nogood learning to develop efficient solvers that can exploit propagation based on either forward checking or maintaining arc-consistency algorithms. Experimental results show that maintaining arc-consistency alone is not competitive with our approach, even if nogood learning and a well known variable ordering are incorporated. It is only by using our cluster-based heuristics can large problems be solved efficiently. The poor performance of maintaining arc-consistency is somewhat surprising, but quite easy to explain.
AB - In this paper we present a novel approach to solving Constraint Satisfaction Problems whose constraint graphs are highly clustered and the graph of clusters is close to being acyclic. Such graphs are encountered in many real world application domains such as configuration, diagnosis, model-based reasoning and scheduling. We present a class of variable ordering heuristics that exploit the clustered structure of the constraint network to inform search. We show how these heuristics can be used in conjunction with nogood learning to develop efficient solvers that can exploit propagation based on either forward checking or maintaining arc-consistency algorithms. Experimental results show that maintaining arc-consistency alone is not competitive with our approach, even if nogood learning and a well known variable ordering are incorporated. It is only by using our cluster-based heuristics can large problems be solved efficiently. The poor performance of maintaining arc-consistency is somewhat surprising, but quite easy to explain.
UR - https://www.scopus.com/pages/publications/38149125553
U2 - 10.1007/978-3-540-73817-6_10
DO - 10.1007/978-3-540-73817-6_10
M3 - Conference contribution
AN - SCOPUS:38149125553
SN - 9783540738169
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 154
EP - 168
BT - Recent Advances in Constraints - 11th Annual ERCIM International Workshop on Constraint Solving and Contraint Logic Programming, CSCLP 2006, Revised Selected and Invited Papers
PB - Springer Verlag
T2 - 11th Annual European Research Consortium for Informatics and Mathematics (ERCIM) International Workshop on Constraint Solving and Constraint Logic Programming, CSCLP 2006
Y2 - 26 June 2006 through 26 June 2006
ER -