Contextual merging of uncertain information for better informed plan selection in BDI systems

Sarah Calderwood; Kevin McAreavey; Weiru Liu; Jun Hong

doi:10.1109/WCICSS.2015.7420326

Contextual merging of uncertain information for better informed plan selection in BDI systems

Sarah Calderwood, Kevin McAreavey, Weiru Liu, Jun Hong

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Sensor information (e.g. temperature, voltage, etc.) obtained from heterogeneous sources in SCADA systems may be uncertain and incomplete, while sensors may be unreliable or conflicting. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the information so that it can be merged in a consistent way. Unfortunately, existing merging operators are not suitable for every situation. We adapt a context-dependent strategy from possibility theory where we determine the context for when to merge using Dempster's rule of combination (i.e. for low conflicting information) and then resort to Dubois and Prade's disjunctive rule to merge information which is highly conflicting. We demonstrate the suitability of our approach with a scenario of a smart grid SCADA system modelled using the Belief-Desire-Intention (BDI) multi-agent framework. In particular, we use the notion of epistemic states to model combined uncertain sensor information for better informed selection of predefined plans.

Original language	English
Title of host publication	Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	64-65
Number of pages	2
ISBN (Print)	978-1-908320-58
DOIs	https://doi.org/10.1109/WCICSS.2015.7420326
Publication status	Published - 29 Feb 2016
Event	2015 World Congress on Industrial Control Systems Security (WCICSS) - London, United Kingdom Duration: 14 Dec 2015 → 14 Dec 2016 http://www.wcicss.org/

Conference

Conference	2015 World Congress on Industrial Control Systems Security (WCICSS)
Country/Territory	United Kingdom
City	London
Period	14/12/2015 → 14/12/2016
Internet address	http://www.wcicss.org/

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/WCICSS.2015.7420326Licence: Unspecified

R1070CSC: Providing Autonomous Capabilities for Evolving SCADA (PACES)
Liu, W. (PI), Hong, J. (CoI) & Sezer, S. (CoI)
01/08/2011 → 31/03/2016
Project: Research

Cite this

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title = "Contextual merging of uncertain information for better informed plan selection in BDI systems",

abstract = "Sensor information (e.g. temperature, voltage, etc.) obtained from heterogeneous sources in SCADA systems may be uncertain and incomplete, while sensors may be unreliable or conflicting. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the information so that it can be merged in a consistent way. Unfortunately, existing merging operators are not suitable for every situation. We adapt a context-dependent strategy from possibility theory where we determine the context for when to merge using Dempster's rule of combination (i.e. for low conflicting information) and then resort to Dubois and Prade's disjunctive rule to merge information which is highly conflicting. We demonstrate the suitability of our approach with a scenario of a smart grid SCADA system modelled using the Belief-Desire-Intention (BDI) multi-agent framework. In particular, we use the notion of epistemic states to model combined uncertain sensor information for better informed selection of predefined plans.",

author = "Sarah Calderwood and Kevin McAreavey and Weiru Liu and Jun Hong",

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booktitle = "Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2015 World Congress on Industrial Control Systems Security (WCICSS) ; Conference date: 14-12-2015 Through 14-12-2016",

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Calderwood, S, McAreavey, K, Liu, W & Hong, J 2016, Contextual merging of uncertain information for better informed plan selection in BDI systems. in Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS). Institute of Electrical and Electronics Engineers Inc., pp. 64-65, 2015 World Congress on Industrial Control Systems Security (WCICSS), London, United Kingdom, 14/12/2015. https://doi.org/10.1109/WCICSS.2015.7420326

Contextual merging of uncertain information for better informed plan selection in BDI systems. / Calderwood, Sarah; McAreavey, Kevin; Liu, Weiru et al.
Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS). Institute of Electrical and Electronics Engineers Inc., 2016. p. 64-65.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Contextual merging of uncertain information for better informed plan selection in BDI systems

AU - Calderwood, Sarah

AU - McAreavey, Kevin

AU - Liu, Weiru

AU - Hong, Jun

PY - 2016/2/29

Y1 - 2016/2/29

N2 - Sensor information (e.g. temperature, voltage, etc.) obtained from heterogeneous sources in SCADA systems may be uncertain and incomplete, while sensors may be unreliable or conflicting. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the information so that it can be merged in a consistent way. Unfortunately, existing merging operators are not suitable for every situation. We adapt a context-dependent strategy from possibility theory where we determine the context for when to merge using Dempster's rule of combination (i.e. for low conflicting information) and then resort to Dubois and Prade's disjunctive rule to merge information which is highly conflicting. We demonstrate the suitability of our approach with a scenario of a smart grid SCADA system modelled using the Belief-Desire-Intention (BDI) multi-agent framework. In particular, we use the notion of epistemic states to model combined uncertain sensor information for better informed selection of predefined plans.

AB - Sensor information (e.g. temperature, voltage, etc.) obtained from heterogeneous sources in SCADA systems may be uncertain and incomplete, while sensors may be unreliable or conflicting. To address these issues we apply Dempster-Shafer (DS) theory to correctly model the information so that it can be merged in a consistent way. Unfortunately, existing merging operators are not suitable for every situation. We adapt a context-dependent strategy from possibility theory where we determine the context for when to merge using Dempster's rule of combination (i.e. for low conflicting information) and then resort to Dubois and Prade's disjunctive rule to merge information which is highly conflicting. We demonstrate the suitability of our approach with a scenario of a smart grid SCADA system modelled using the Belief-Desire-Intention (BDI) multi-agent framework. In particular, we use the notion of epistemic states to model combined uncertain sensor information for better informed selection of predefined plans.

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M3 - Conference contribution

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BT - Proceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS)

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T2 - 2015 World Congress on Industrial Control Systems Security (WCICSS)

Y2 - 14 December 2015 through 14 December 2016

ER -

Contextual merging of uncertain information for better informed plan selection in BDI systems

Abstract

Conference

UN SDGs

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Projects

R1070CSC: Providing Autonomous Capabilities for Evolving SCADA (PACES)

Cite this