Conference item
Probabilistic timed ATL
- Abstract:
- Weconsider strategic reasoning for multi-agent systems modelled as networks of continuous-time probabilistic timed automata (TA) with asynchronous execution (PCAMAS) in the setting of imperfect information. We define PTATL, a probabilistic extension of the alternating-time timed temporal logic TATL, which is interpreted over PCAMAS. Focusing on memoryless strategies of agents with imperfect information, both probabilistic (irP) and deterministic (irp), we establish theoretical results regarding the computational complexity of model checking for the proposed logic: between PSPACE and EXPTIME for PTATLirp, and in 2EXPTIME for PTATLirP. We demonstrate the practical feasibility of verification for PTATLirp formulas through a novel proof-of-concept combination of state-of-the-art tools IMITATOR and PRISM on a scalable benchmark, with encouraging results.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 636.0KB, Terms of use)
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- Publication website:
- https://dl.acm.org/doi/10.5555/3709347.3743625
Authors
+ European Research Council
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- Funder identifier:
- https://ror.org/0472cxd90
- Grant:
- 834115
- Publisher:
- Association for Computing Machinery
- Host title:
- AAMAS '25: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems
- Pages:
- 1051-1059
- Publication date:
- 2025-06-05
- Event title:
- 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025)
- Event location:
- Detroit, Michigan, USA
- Event website:
- https://aamas2025.org/
- Event start date:
- 2025-05-19
- Event end date:
- 2025-05-23
- EISSN:
-
1558-2914
- ISSN:
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1548-8403
- Language:
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English
- Keywords:
- Pubs id:
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2247710
- UUID:
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uuid_f0673024-d8aa-46f0-b0fb-79ec2bc0a025
- Local pid:
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pubs:2247710
- Deposit date:
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2025-11-10
- ARK identifier:
Terms of use
- Copyright holder:
- International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
- Copyright date:
- 2025
- Rights statement:
- © 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). This work is licensed under a Creative Commons Attribution International 4.0 License.
- Notes:
- This paper was presented at the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025), 19th-23rd May 2025, Detroit, Michigan, USA. For the purpose of open access, and in fulfilment of the obligations arising from the grant agreement, the authors have applied CC BY 4.0 license to any Author Accepted Manuscript version arising from this submission.
- Licence:
- CC Attribution (CC BY)
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