{"type":"ScholarlyArticle","title":"Robustness Computation of Dynamic Controllability in Probabilistic Temporal Networks with Ordinary Distributions","authors":[{"name":"Michael Saint-Guillain","slug":"michael-saint-guillain","given":"Michael","family":"Saint-Guillain","affiliation":"Université Catholique de Louvain (UCLouvain) , Belgium"},{"name":"Tiago Stegun Vaquero","slug":"tiago-stegun-vaquero","given":"Tiago","family":"Stegun Vaquero","affiliation":"Jet Propulsion Laboratory , California Institute Technology , CA , USA"},{"name":"Jagriti Agrawal","slug":"jagriti-agrawal","given":"Jagriti","family":"Agrawal","affiliation":"Jet Propulsion Laboratory , California Institute Technology , CA , USA"},{"name":"Steve Chien","slug":"steve-chien","given":"Steve","family":"Chien","affiliation":"Jet Propulsion Laboratory , California Institute Technology , CA , USA"}],"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.","doi":"10.24963/ijcai.2020/576","url":"https://ijcai.org/proceedings/2020/576","pdf":"https://ijcai.org/proceedings/2020/0576.pdf","markdown":"https://ijcai.org/proceedings/2020/576.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/576","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Planning and Scheduling","track":"Main track","number":576,"pages":{"first":4168,"last":4175},"keywords":["Planning and Scheduling: Robot Planning","Planning and Scheduling: Temporal and Hybrid planning","Planning and Scheduling: Planning under Uncertainty"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24850/file_24850.m3u8","length_sec":282,"provider":"hls"}],"references":[{"position":1,"raw":"[Agrawal et al., 2019] Jagriti Agrawal, Wayne Chi, Steve Chien, Gregg Rabideau, Stephen Khun, and Daniel Gaines. 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