TY - GEN
T1 - Reformulating CSPs for scalability with application to geospatial reasoning
AU - Bayer, Kenneth M.
AU - Michalowski, Martin
AU - Choueiry, Berthe Y.
AU - Knoblock, Craig A.
PY - 2007
Y1 - 2007
N2 - While many real-world combinatorial problems can be advantageously modeled and solved using Constraint Programming, scalability remains a major issue in practice. Constraint models that accurately reflect the inherent structure of a problem, solvers that exploit the properties of this structure, and reformulation techniques that modify the problem encoding to reduce the cost of problem solving are typically used to overcome the complexity barrier. In this paper, we investigate such approaches in a geospatial reasoning task, the building-identification problem (BID), introduced and modeled as a Constraint Satisfaction Problem by Michalowski and Knoblock [1]. We introduce an improved constraint model, a custom solver for this problem, and a number of reformulation techniques that modify various aspects of the problem encoding to improve scalability. We show how interleaving these reformulations with the various stages of the solver allows us to solve much larger BID problems than was previously possible. Importantly, we describe the usefulness of our reformulations techniques for general Constraint Satisfaction Problems, beyond the BID application.
AB - While many real-world combinatorial problems can be advantageously modeled and solved using Constraint Programming, scalability remains a major issue in practice. Constraint models that accurately reflect the inherent structure of a problem, solvers that exploit the properties of this structure, and reformulation techniques that modify the problem encoding to reduce the cost of problem solving are typically used to overcome the complexity barrier. In this paper, we investigate such approaches in a geospatial reasoning task, the building-identification problem (BID), introduced and modeled as a Constraint Satisfaction Problem by Michalowski and Knoblock [1]. We introduce an improved constraint model, a custom solver for this problem, and a number of reformulation techniques that modify various aspects of the problem encoding to improve scalability. We show how interleaving these reformulations with the various stages of the solver allows us to solve much larger BID problems than was previously possible. Importantly, we describe the usefulness of our reformulations techniques for general Constraint Satisfaction Problems, beyond the BID application.
UR - http://www.scopus.com/inward/record.url?scp=38149051495&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=38149051495&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-74970-7_14
DO - 10.1007/978-3-540-74970-7_14
M3 - Conference contribution
AN - SCOPUS:38149051495
SN - 3540749691
SN - 9783540749691
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 164
EP - 179
BT - Principles and Practice of Constraint Programming - CP 2007 - 13th International Conference, CP 2007, Proceedings
PB - Springer Verlag
T2 - 13th International Conference on Principles and Practice of Constraint Programming, CP 2007
Y2 - 23 September 2007 through 27 September 2007
ER -