Building Reusable Repertoires for Stochastic Self-* Planners

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@InProceedings{Kinneer:2020:ACSOS,

• author = "Cody Kinneer and Rijnard {van Tonder} and David Garlan and Claire {Le Goues}",
• title = "Building Reusable Repertoires for Stochastic Self-* Planners",
• booktitle = "2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS)",
• year = "2020",
• pages = "222--231",
• month = aug,
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1109/ACSOS49614.2020.00045",
• abstract = "Plan reuse is a promising approach for enabling self-* systems to effectively adapt to unexpected changes, such as evolving existing adaptation strategies after an unexpected change using stochastic search. An ideal self-* planner should be able to reuse repertoires of adaptation strategies, but this is challenging due to the evaluation overhead. For effective reuse, a repertoire should be both (a) likely to generalize to future situations, and (b) cost effective to evaluate. In this work, we present an approach inspired by chaos engineering for generating a diverse set of adaptation strategies to reuse, and we explore two analysis approaches based on clone detection and syntactic transformation for constructing repertoires of adaptation strategies that are likely to be amenable to reuse in stochastic search self-*planners. An evaluation of the proposed approaches on a simulated system inspired by Amazon Web Services shows planning effectiveness improved by up to 2percent and reveals tradeoffs in planning timeliness and optimality.",
• notes = "Also known as \cite{9196217}",

}

Genetic Programming entries for Cody Kinneer Rijnard van Tonder David Garlan Claire Le Goues

Citations