SOAR: an architecture for general intelligence
Artificial Intelligence
The Adaptive Constraint Engine
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Neural networks to guide the selection of heuristics within constraint satisfaction problems
MCPR'11 Proceedings of the Third Mexican conference on Pattern recognition
Variable and value ordering decision matrix hyper-heuristics: a local improvement approach
MICAI'11 Proceedings of the 10th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
Improving the performance of vector hyper-heuristics through local search
Proceedings of the 14th annual conference on Genetic and evolutionary computation
Learning vector quantization for variable ordering in constraint satisfaction problems
Pattern Recognition Letters
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This paper explores the use of hyper-heuristics for variable and value ordering in binary Constraint Satisfaction Problems (CSP). Specifically, we describe the use of a symbolic cognitive architecture, augmented with constraint based reasoning as the hyper-heuristic machine learning framework. The underlying design motivation of our approach is to "do more with less." Specifically, the approach seeks to minimize the number of low level heuristics encoded yet dramatically expand the expressiveness of the hyper-heuristic by encoding the constituent measures of each heuristic, thereby providing more opportunities to achieve improved solutions. Further, the use of a symbolic cognitive architecture allows us to encode hierarchical preferences which extend the effectiveness of the hyper-heuristic across problem types. Empirical experiments are conducted to generate and test hyper-heuristics for two benchmark CSP problem types: Map Coloring; and, Job Shop Scheduling. Results suggest that the hyper-heuristic approach provides a dramatically higher level of representational granularity allowing superior intra-problem and inter-problem solutions to be secured over traditional combinations of variable and value ordering heuristics.