Constraint Processing
Approximate compilation for embedded model-based reasoning
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Superstabilizing, fault-containing distributed combinatorial optimization
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Diagnosing tree-decomposable circuits
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Task-dependent qualitative domain abstraction
Artificial Intelligence - Special volume on reformulation
An analysis of map-based abstraction and refinement
SARA'07 Proceedings of the 7th International conference on Abstraction, reformulation, and approximation
Mini-bucket heuristics for improved search
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Solving strong-fault diagnostic models by model relaxation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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Many tasks in artificial intelligence, such as diagnosis, planning, and reconfiguration, can be framed as constraint optimization problems. However, running constraint optimization within embedded systems requires methods to curb the resource requirements in terms of memory and run-time. In this paper, we present a novel method to control the memory requirements of message-passing algorithms that decompose the problem into clusters and use dynamic programming to compute approximate solutions. It can be viewed as an extension of the previously proposed mini-bucket scheme, which limits message size simply by omitting constraints from the messages. Our algorithm instead adaptively abstracts constraints, and we argue that this allows for a more finegrained control of resources particularly for constraints of higher arity and variables with large domains that often occur in models of technical systems. Preliminary experiments with a diagnosis model of NASA's EO-1 satellite appear promising.