IJCAI 2020poster0 citations

Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions

Michael Saint-Guillain, Tiago Stegun Vaquero, Jagriti Agrawal, Steve Chien

Abstract

Most existing works in Probabilistic Simple Temporal Networks (PSTNs) base their frameworks on well-defined probability distributions. This paper addresses on PSTN Dynamic Controllability (DC) robustness measure, i.e. the execution success probability of a network under dynamic control. We consider PSTNs where the probability distributions of the contingent edges are ordinary distributed (e.g. non-parametric, non-symmetric). We introduce the concepts of dispatching protocol (DP) as well as DP-robustness, the probability of success under a predefined dynamic policy. We propose a fixed-parameter pseudo-polynomial time algorithm to compute the exact DP-robustness of any PSTN under NextFirst protocol, and apply to various PSTN datasets, including the real case of planetary exploration in the context of the Mars 2020 rover, and propose an original structural analysis.

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BibTeX
@inproceedings{ijcai2020p576,
  title     = {Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions},
  author    = {Saint-Guillain, Michael and Stegun Vaquero, Tiago and Agrawal, Jagriti and Chien, Steve},
  booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
               Artificial Intelligence, {IJCAI-20}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {Christian Bessiere},
  pages     = {4168--4175},
  year      = {2020},
  month     = {7},
  note      = {Main track},
  doi       = {10.24963/ijcai.2020/576},
  url       = {https://doi.org/10.24963/ijcai.2020/576},
}