Deriving Stochastic Properties from Behavior Models Defined by Monterey Phoenix
Loading...
Authors
Quartuccio, John
Giammarco, Kristin
Auguston, Mikhail
Subjects
behavior model
Bayesian belief network
model checking
system of systems
lightweight formal methods
Bayesian belief network
model checking
system of systems
lightweight formal methods
Advisors
Date of Issue
2017-07-31
Date
Publisher
IEEE
Language
Abstract
Stochastic properties of behavior models are of interest to
the developer of a System of Systems (SoS) in order to gain insight to
the likelihood of potential outcomes of the system. Constraints added
to the system introduce changes to the inherent dependencies within a
representative Bayesian belief network; thereby impacting the system.
This paper defines a probability process model that may be used to
identify the probability of outcomes compliant with behavior models
defined in Monterey Phoenix (MP), with constraints added to the model.
Type
Article
Description
The article of record as published may be found a http://doi.org/10.1109/SYSOSE.2017.7994961
Published in: 2017 12th System of Systems Engineering Conference (SoSE); Date of Conference: 18-21 June 2017
Published in: 2017 12th System of Systems Engineering Conference (SoSE); Date of Conference: 18-21 June 2017
Series/Report No
Department
Systems Engineering (SE)
Computer Science (CS)
Organization
Naval Postgraduate School (U.S.)
Identifiers
NPS Report Number
Sponsors
Funder
Format
6 p.
Citation
Quartuccio, John, Kristin Giammarco, and Mikhail Auguston. "Deriving stochastic properties from behavior models defined by Monterey Phoenix." System of Systems Engineering Conference (SoSE), 2017 12th. IEEE, 2017.
Distribution Statement
Rights
This publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.