Autonomous Search (AS) is a special feature allowing systems to improve their performance by self-adaptation. This approach has been recently adapted to Constraint Programming (CP) reporting promising results. However, as the research lies in a preliminary stage there is a lack of implementation frameworks and architectures. This hinders the research progress, which in particular, requires a considerable work in terms of experimentation. In this paper, we propose a new framework for implementing AS in CP. It allows a dynamic self-adaptation of the classic CP solving process and an easy update of its components, allowing developers to define their own AS-CP approaches. We believe this will help researchers to perform new AS experiments, and as a consequence to improve the current preliminary results. © 2011 Springer-Verlag.
CITATION STYLE
Crawford, B., Soto, R., Montecinos, M., Castro, C., & Monfroy, E. (2011). A framework for autonomous search in the Eclipse solver. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6703 LNAI, pp. 79–84). https://doi.org/10.1007/978-3-642-21822-4_9
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