Autonomous search in constraint satisfaction via black hole: A performance evaluation using different choice functions

Ricardo Soto, Broderick Crawford, Rodrigo Olivares, Stefanie Niklander, Eduardo Olguín

Resultado de la investigación: Capítulo del libro/informe/acta de congresoCapítulorevisión exhaustiva

2 Citas (Scopus)

Resumen

Autonomous Search is a modern technique aimed at introducing self-adjusting features to problem-solvers. In the context of constraint satisfaction, the idea is to let the solver engine to autonomously replace its solving strategies by more promising ones when poor performances are identified. The replacement is controlled by a choice function, which takes decisions based on information collected during solving time. However, the design of choice functions can be done in very different ways, leading of course to very different resolution processes. In this paper, we present a performance evaluation of 16 rigorously designed choice functions. Our goal is to provide new and interesting knowledge about the behavior of such functions in autonomous search architectures. To this end, we employ a set of well-known benchmarks that share general features that may be present on most constraint satisfaction and optimization problems. We believe this information will be useful in order to design better autonomous search systems for constraint satisfaction.

Idioma originalInglés
Título de la publicación alojadaLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorialSpringer Verlag
Páginas56-65
Número de páginas10
DOI
EstadoPublicada - 2016
Publicado de forma externa

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen9712 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

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