Imperial College London
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ProfessorMichaelHuth

Faculty of Engineering, Department of Computing

Head of the Department of Computing
 
 
 
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Contact

 

m.huth Website

 
 
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Location

 

Huxley 566Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Huth:2016,
author = {Huth, MRA and Beaumont and Day, E and Evans, N and Haworth, S and Plant, T and Roberts, C},
publisher = {Institute of Nuclear Materials Management},
title = {An in-depth case study: modelling an information barrier with Bayesian Belief Networks},
url = {http://hdl.handle.net/10044/1/41828},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - We present in detail a quantitative Bayesian Belief Network (BBN) model of the use of aninformation barrier system during a nuclear arms control inspection, and an analysis of this model usingthe capabilities of a Satis ability Modulo Theory (SMT) solver. Arms control veri cation processes do notin practice allow the parties involved to gather complete information about each other, and therefore anymodel we use must be able to cope with the limited information, subjective assessment and uncertaintyin this domain. We have previously extended BBNs to allow this kind of uncertainty in parameter values(such as probabilities) to be re ected; theseconstrainedBBNs (cBBNs) o er the potential for more robustmodelling, which in that study we demonstrated with a simple information barrier model. We now presenta  much  more  detailed  model  of  a  similar  veri cation  process,  based  on  the  technical  capabilities  anddeployment concept of the UK-Norway Initiative (UKNI) Information Barrier system, demonstrating thescalability  of  our  previously-presented  approach.  We  discuss  facets  of  the  model  itself  in  detail,  beforeanalysing pertinent questions of interest to give examples of the power of this approach.
AU  - Huth,MRA
AU  - Beaumont
AU  - Day,E
AU  - Evans,N
AU  - Haworth,S
AU  - Plant,T
AU  - Roberts,C
PB  - Institute of Nuclear Materials Management
PY  - 2016///
TI  - An in-depth case study: modelling an information barrier with Bayesian Belief Networks
UR  - http://hdl.handle.net/10044/1/41828
ER  -
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