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Inference in constraint programming is usually based on the deductions generated by individual constraints which are then communicated to other constraints through domain filtering. Frequently we find that this is a too coarse-grained form of communication since constraints could exchange more powerful forms of deductions that could help reduce the search effort. In this paper we propose a particular...
We propose a framework which we call stochastic off-line programming (SOP). The idea is to embed the development of combinatorial algorithms in an off-line learning environment which helps the developer choose heuristic advisors that guide the search for satisfying or optimal solutions. In particular, we consider the case where the developer has several heuristic advisors available. Rather than selecting...
Spatial heterogeneity in fields may affect the outcome of experiments. The conventional randomized allocation of treatments to plots may cause bias and variable precision in the presence of trends (including periodicity) and spatial autocorrelation. Agricultural scientists appear to mostly use conventional experimental designs that are susceptible to adverse affects from field variability. The objectives...
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