On Quantified Observability Analysis in Multi-Agent Systems

Chunyan Mu, Jun Pang

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

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Abstract

In multiagent systems (MASs), agents’ observation upon system behaviours may improve the overall team performance, but may also leak sensitive information to an observer. A quantified observability analysis can thus be useful to assist decision-making in MASs by operators seeking to optimise the relationship between performance effectiveness and information exposure through the observation in practice. This paper presents a novel approach to quantitatively analysing the observability properties in MASs. The concept of opacity is applied to formally express the characterisation of observability in MASs modelled as partially observable multiagent systems (POMASs). We propose a temporal logic oPATL to reason about agents’ observability with quantitative goals, which capture the probability of information transparency of system behaviours to an observer, and develop verification techniques for quantitatively analysing such properties.We also implement the approach as an extension of the probabilistic model checker PRISM, and illustrate its applicability via several examples.
Original languageEnglish
Title of host publication27th European Conference on Artificial Intelligence
PublisherIOS Press
Number of pages8
Publication statusAccepted/In press - 15 Jul 2023
EventECAI 2023 - Krakow
Duration: 30 Sept 20235 Oct 2023

Publication series

NameFrontiers in Artificial Intelligence and Applications
PublisherIOS Press
ISSN (Print)0922-6389
ISSN (Electronic)1879-8314

Conference

ConferenceECAI 2023
Period30/09/235/10/23

Keywords

  • Multiagent systems
  • observability
  • information security
  • verification

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