Probabilistic Resilience in Hidden Markov Models

4th International Conference on Manufacturing, Optimization, Industrial and Material Engineering

Panerati, Jacopo^1 ; Beltrame, Giovanni^1 ; Schwind, Nicolas^2 ; Zeltner, Stefan^3 ; Inoue, Katsumi^4

Polytechnique Montreal, 2900 boul. edouard-Montpetit, Montréal

QC

H3T 1J4, Canada^1

Transdisciplinary Research Integration Center, 10-3, Midori-cho, Tachikawa, Tokyo, Japan^2

Bloomberg L.P., 731 Lexington Ave, New York

NY

10022, United States^3

National Institute of Informatics, 2-1-2 Hitotsubashi, Chiyoda, Tokyo

101-0003, Japan^4

关键词: Design and analysis;    Environmental science;    Important features;    Inference algorithm;    Partial observability;    Probabilistic framework;    Resilient networks;    Stochastic evolution;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/131/1/012007/pdf
DOI  :  10.1088/1757-899X/131/1/012007

来源: IOP

【 摘 要 】

Originally defined in the context of ecological systems and environmental sciences, resilience has grown to be a property of major interest for the design and analysis of many other complex systems: resilient networks and robotics systems other the desirable capability of absorbing disruption and transforming in response to external shocks, while still providing the services they were designed for. Starting from an existing formalization of resilience for constraint-based systems, we develop a probabilistic framework based on hidden Markov models. In doing so, we introduce two new important features: stochastic evolution and partial observability. Using our framework, we formalize a methodology for the evaluation of probabilities associated with generic properties, we describe an efficient algorithm for the computation of its essential inference step, and show that its complexity is comparable to other state-of-the-art inference algorithms.