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Introducing statistical consistency for infinite chance constraints

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A Correction to this article was published on 05 May 2018

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Abstract

In this paper, we propose a novel notion of statistical consistency for single-stage Stochastic Constraint Satisfaction Problems (SCSPs) in which some of the random variables’ support set is infinite. The essence of this novel notion of local consistency is to be able to make an inference in the presence of infinite scenarios in an uncertain environment. This inference would be based on a restricted finite subset of scenarios with a certain confidence level and a threshold tolerance error. The confidence level is the probability that characterizes the extend to which our inference — based on a subset of scenarios — is correct. The threshold tolerance error is the error range that we can tolerate while making such an inference. We propose a novel statistical consistency enforcing algorithm that is based on sound statistical inference; and experimentally show how to prune inconsistent values in the presence of an infinite set of scenarios.

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Change history

  • 05 May 2018

    The original version of this article unfortunately contained a mistake. The name “Abdelwahad Rebaii” should be corrected to “Abdelwaheb Rebai”.

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Author information

Authors and Affiliations

  1. Modils Research Lab, FSEG, University of Sfax, Sfax, Tunisia

    Imen Zghidi & Abdelwaheb Rebai

  2. CES, ENIS, University of Sfax, Sfax, Tunisia

    Brahim Hnich

  3. Department of CS, Monastir University, Monastir, Tunisia

    Brahim Hnich

Authors
  1. Imen Zghidi
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  2. Brahim Hnich
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  3. Abdelwaheb Rebai
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Corresponding author

Correspondence to Imen Zghidi.

Additional information

The original version of this article was revised: The name “Abdelwahad Rebaii” should be corrected to “Abdelwaheb Rebai”. The correct name is now shown here.

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Zghidi, I., Hnich, B. & Rebai, A. Introducing statistical consistency for infinite chance constraints. Ann Math Artif Intell 83, 165–181 (2018). https://doi.org/10.1007/s10472-018-9572-3

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  • DOI: https://doi.org/10.1007/s10472-018-9572-3

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