CPlan: a constraint programming approach to planning
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
A Sufficient Condition for Backtrack-Free Search
Journal of the ACM (JACM)
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Proceedings of the 12th annual ACM international workshop on Geographic information systems
The Alignment Template Approach to Statistical Machine Translation
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Backbones and backdoors in satisfiability
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A SAT-based version space algorithm for acquiring constraint satisfaction problems
ECML'05 Proceedings of the 16th European conference on Machine Learning
Integrating gazetteers and remote sensed imagery
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
LEARNING AND VERIFYING SAFETY CONSTRAINTS FOR PLANNERS IN A KNOWLEDGE-IMPOVERISHED SYSTEM
Computational Intelligence
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The building identification (BID) problem is based on a process that uses publicly available information to automatically assign addresses to buildings in satellite imagery. In previous work, we have shown the advantages of casting the BID problem as a Constraint Satisfaction Problem (CSP) using the same generic constraint-model to represent all problem instances. However, a generic model is unable to represent with the necessary precision the addressing variations throughout the world, limiting the applicability of our previous approach. In this paper, we describe the end-to-end process used to solve the BID with a new model-generation technique that uses instance-specific information to automatically infer a representative constraint model of the BID. This inferred model is used by our custom constraint solver to identify buildings in satellite imagery more efficiently and with higher precision than using a single model. We evaluate our approach on El Segundo California, and empirically demonstrate its effectiveness for geographic areas larger than previously tested. We conclude with a discussion of the generality of our approach, and present directions for future work.