TY - GEN
T1 - Alternating-time Temporal Logic with Stochastic Abilities
AU - Ballot, Gabriel
AU - Malvone, Vadim
AU - Leneutre, Jean
AU - Ma, Jingxuan
AU - Leslous, Mourad
N1 - Publisher Copyright:
© 2025 International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org).
PY - 2025/1/1
Y1 - 2025/1/1
N2 - Multi-agent systems strategic verification is a branch of formal methods to model, reason about, and verify strategic behavior in complex environments. The notion of agent capacity was introduced alongside the strategic logic CapATL to model multi-agent systems in which each player may exhibit diverse abilities or profiles. These capacities can represent various aspects, such as an agent's experience level, personality traits, type, or version. In real-world applications, domain knowledge or prior statistical analyses may provide a probability distribution over the possible profiles of each agent. This leads to the concept of stochastic abilities, where capacities are assigned probabilistically, yet remain private to other agents. In this context, we introduce a novel probabilistic strategic logic, called ATL-SA, that allows the expression of properties concerning the likelihood that agents or coalitions can achieve specific temporal objectives under uncertainty about their capacities. We study the upper and lower complexity bounds of ATL-SA model checking and demonstrate its practical applicability through a use case in cybersecurity, showcasing its potential for analysing systems with probabilistic agent profiles.
AB - Multi-agent systems strategic verification is a branch of formal methods to model, reason about, and verify strategic behavior in complex environments. The notion of agent capacity was introduced alongside the strategic logic CapATL to model multi-agent systems in which each player may exhibit diverse abilities or profiles. These capacities can represent various aspects, such as an agent's experience level, personality traits, type, or version. In real-world applications, domain knowledge or prior statistical analyses may provide a probability distribution over the possible profiles of each agent. This leads to the concept of stochastic abilities, where capacities are assigned probabilistically, yet remain private to other agents. In this context, we introduce a novel probabilistic strategic logic, called ATL-SA, that allows the expression of properties concerning the likelihood that agents or coalitions can achieve specific temporal objectives under uncertainty about their capacities. We study the upper and lower complexity bounds of ATL-SA model checking and demonstrate its practical applicability through a use case in cybersecurity, showcasing its potential for analysing systems with probabilistic agent profiles.
KW - Cybersecurity
KW - Model Checking
KW - Strategic Reasoning
UR - https://www.scopus.com/pages/publications/105009811426
M3 - Conference contribution
AN - SCOPUS:105009811426
T3 - Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
SP - 214
EP - 222
BT - Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
A2 - Vorobeychik, Yevgeniy
A2 - Das, Sanmay
A2 - Nowe, Ann
PB - International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS)
T2 - 24th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2025
Y2 - 19 May 2025 through 23 May 2025
ER -