Towards Probabilistic Strategic Timed CTL

2026-02-11

AI Summary

This work addresses the challenge of probabilistic verification of strategic behaviors in continuous-time, asynchronously executed stochastic multi-agent systems by proposing PSTCTL—a probabilistic extension of Strategic Temporal Computation Tree Logic (STCTL). For the first time, PSTCTL integrates probabilistic semantics into STCTL, enabling formal modeling and verification of multi-agent strategies under continuous-time and asynchronous execution semantics. The authors develop a verification framework based on irP-strategies, rigorously define the syntax and semantics of PSTCTL within this framework, and establish its verifiability under such strategies. This contribution provides a foundational theoretical basis for trustworthy strategic analysis of complex stochastic multi-agent systems.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Reasoning under Uncertainty: Stochastic Optimization
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Agentic search

Abstract

We define PSTCTL, a probabilistic variant of Strategic Timed CTL (STCTL), interpreted over stochastic multi-agent systems with continuous time and asynchronous execution semantics. STCTL extends TCTL with strategic operators in the style of ATL. Moreover, we demonstrate the feasibility of verification with irP-strategies.

Problem

• probabilistic verification
• strategic timed CTL
• stochastic multi-agent systems
• continuous time
• asynchronous execution

Innovation

• PSTCTL
• probabilistic verification
• strategic timed logic
• stochastic multi-agent systems
• irP-strategies