Programming Constraint Inference Engines
Author: Christian Schulte
Editor: Gert Smolka
Existing constraint programming systems offer a fixed set of inference
engines implementing search strategies such as single, all, and best
solution search. This is unfortunate, since new engines cannot be
integrated by the user. The paper presents first-class computation
spaces as abstractions with which the user can program inference engines
at a high level. Using computation spaces, the paper covers several
inference engines ranging from standard search strategies to techniques
new to constraint programming, including limited discrepancy search,
visual search, and saturation. Saturation is an inference method for
tautology-checking used in industrial practice. Computation spaces have
shown their practicability in the constraint programming system Oz.
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