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 proceedingConference Contribution (Conference Proceeding)

8 Downloads (Pure)

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 languageEnglish
Title of host publicationProceedings of the 2015 World Congress on Industrial Control Systems Security (WCICSS)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages64-65
Number of pages2
ISBN (Print)978-1-908320-58
DOIs
Publication statusPublished - 1 Feb 2016

Fingerprint

Dive into the research topics of 'Contextual merging of uncertain information for better informed plan selection in BDI systems'. Together they form a unique fingerprint.

Cite this