Abstract
In Constraint Satisfaction Problems (CSP), variable ordering heuristics help to increase efficiency. Applying an appropriate heuristic can increase the performance of CSP solvers. On the other hand, if we apply specific heuristics for similar CSPs, CSP solver performance could be further improved. Similar CSPs can be grouped into same clusters. For each cluster, appropriate heuristics can be found by applying a local search. Thus, when a new CSP is created, the corresponding cluster can be found and the pre-calculated heuristics for the cluster can be applied. In this paper, we propose a new method for constraint solving which is called Cluster Specific Heuristic (CSH). We present and evaluate our method on the basis of example CSPs.
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The work presented in this paper has been conducted within the scope of the European Union Horizon 2020 research project AGILE (Adoptive Gateways for dIverse MuLtiple Environments – www.agile-project-iot.eu.).
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Our experiments have been conducted on an Intel Core i5-5200U PC, 2.20 GHz processor, 8 GB RAM, and 64 bit Windows 7 Operating System and Java Run-time Environment 1.8.0.
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Erdeniz, S.P., Felfernig, A., Atas, M., Tran, T.N.T., Jeran, M., Stettinger, M. (2017). Cluster-Specific Heuristics for Constraint Solving. In: Benferhat, S., Tabia, K., Ali, M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE 2017. Lecture Notes in Computer Science(), vol 10350. Springer, Cham. https://doi.org/10.1007/978-3-319-60042-0_3
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