Rational Verification with Quantitative Probabilistic Goals

David Hyland, Krishna Shankaranarayanan, Julian Gutierrez, Michael Wooldridge

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Abstract

We study the rational verification problem for multi-agent systems in a setting where agents have quantitative probabilistic goals. We use concurrent stochastic games to model multi-agent systems and assume players desire to maximise the probability of satisfying their goals, specified using Linear Temporal Logic (LTL). The main decision problem in this setting is whether a given LTL formula is almost surely satisfied on some pure Nash equilibrium of a given game. We prove that this problem is undecidable in the general case, and then characterise the complexity of this problem under various restrictions on strategies. We also study the problem of deciding whether a given strategy profile is a Nash equilibrium in a given game and show that, unlike the previous verification problem, this question is decidable for several common strategy models.

Original languageEnglish
Title of host publicationProceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2024)
EditorsFabian Lorig, Yingqian Zhang
Place of PublicationLiverpool UK
PublisherInternational Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
Pages871-879
Number of pages9
ISBN (Electronic)9781400704864
Publication statusPublished - 2024
EventInternational Conference on Autonomous Agents and Multiagent Systems 2024 - Auckland, New Zealand
Duration: 6 May 202410 May 2024
Conference number: 23rd
https://www.ifaamas.org/Proceedings/aamas2024/ (Proceedings)
https://www.aamas2024-conference.auckland.ac.nz/ (Website)

Conference

ConferenceInternational Conference on Autonomous Agents and Multiagent Systems 2024
Abbreviated titleAAMAS 2024
Country/TerritoryNew Zealand
CityAuckland
Period6/05/2410/05/24
Internet address

Keywords

  • computational game theory
  • formal verification
  • Linear Temporal Logic
  • Multi-agent systems
  • quantitative probabilistic goals

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