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Exploring the Impact of Early Decisions in Variable Ordering for Constraint Satisfaction Problems
José Carlos Ortiz-Bayliss1, Ivan Amaya1, Santiago Enrique Conant-Pablos1
1Tecnológico de Monterrey, Escuela de Ingeniería y Ciencias, Ave. Eugenio Garza Sada 2501 Sur, Col. Tecnológico, 64849 Monterrey, NL, Mexico.
Computational Intelligence and Neuroscience
|April 24, 2018
Summary
Choosing the right first variable is crucial for solving constraint satisfaction problems (CSPs). This study reveals common heuristics often fail, but a simple method improves performance.
Area of Science:
- Artificial Intelligence
- Computer Science
Background:
- Constraint satisfaction problems (CSPs) are frequently solved using heuristics for variable ordering.
- Variable ordering significantly impacts search cost, yet the effect of initial variable choices remains understudied.
Purpose of the Study:
- To investigate the cost associated with finding high-quality variable orderings in CSPs.
- To analyze the impact of initial variable selection on overall search cost.
- To evaluate the effectiveness of existing variable ordering heuristics.
Main Methods:
- Exploration of variable ordering costs in CSPs.
- Comparative analysis of orderings generated by common heuristics.
- Assessment of the influence of early variable instantiation decisions on search performance.
Main Results:
- The initial variable choice critically affects the total search cost in CSPs.
- Many standard variable ordering heuristics do not adequately select the first variable.
- A proposed simple method enhances the performance of existing heuristics by improving early decisions.
Conclusions:
- First variable selection is paramount for efficient CSP solving.
- Current heuristics require refinement to optimize initial variable choices.
- The developed method offers a practical way to improve heuristic performance in CSPs.
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