Probabilistic Strategy Logic with Degrees of Observability

Chunyan Mu, Nima Motamed, Natasha Alechina, Brian Logan

Research output: Chapter in Book/Report/Conference proceedingConference Article in proceedingAcademicpeer-review

Abstract

There has been considerable work on reasoning about the strategic ability of agents under imperfect information. However, existing logics such as Probabilistic Strategy Logic are unable to express properties relating to information transparency. Information transparency concerns the extent to which agents’ actions and behaviours are observable by other agents. Reasoning about information transparency is useful in many domains including security, privacy, and decision-making. In this paper, we present a formal framework for reasoning about information transparency properties in stochastic multi-agent systems. We extend Probabilistic Strategy Logic with new observability operators that capture the degree of observability of temporal properties by agents. We show that the model checking problem for the resulting logic is decidable.

Original languageEnglish
Title of host publicationSpecial Track on AI Alignment
EditorsToby Walsh, Julie Shah, Zico Kolter
PublisherAssociation for the Advancement of Artificial Intelligence
Pages15092-15099
Number of pages8
Edition14
ISBN (Electronic)9781577358978
DOIs
Publication statusPublished - 11 Apr 2025
Event39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025 - Philadelphia, United States
Duration: 25 Feb 20254 Mar 2025
Conference number: 39
https://aaai.org/conference/aaai/aaai-25/

Publication series

SeriesProceedings of the AAAI Conference on Artificial Intelligence
Number14
Volume39
ISSN2159-5399

Conference

Conference39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025
Abbreviated titleAAAI-25
Country/TerritoryUnited States
CityPhiladelphia
Period25/02/254/03/25
Internet address

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