Probabilistic Strategy Logic with Degrees of Observability

2024-12-19

AI Summary

Formalizing information transparency—i.e., quantifying agents’ observability of others’ states and actions—in stochastic multi-agent systems remains challenging. Method: This paper introduces, for the first time, an observability operator into probabilistic strategy logic, enabling quantitative specification of observational granularity over temporal properties. We propose a hybrid semantic framework integrating probabilistic computation tree logic (PCTL) with strategy logic, jointly modeling observational relations and probabilistic transition systems to formally capture transparency under incomplete information. Contribution/Results: We design a decidable model-checking algorithm for this logic, prove its exponential-time complexity, and demonstrate its applicability to automated verification of transparency constraints in safety- and privacy-critical scenarios—thereby establishing the first formal reasoning foundation for transparency in probabilistic multi-agent systems.

Technology Category

• Reasoning under Uncertainty: Relational Probabilistic Models
• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Security and Privacy: Data transparency and provenance
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

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.

Problem

• Multi-Agent Systems
• Information Transparency
• Randomness

Innovation

• Probabilistic Strategy Logic
• Information Transparency Assessment
• Multi-Agent Systems