Responsibility-aware Strategic Reasoning in Probabilistic Multi-Agent Systems
Chunyan Mu, Muhammad Najib, Nir Oren
TL;DR
This work addresses the challenge of responsibility-aware strategic reasoning in probabilistic multi-agent systems by extending Probabilistic Alternating-time Temporal Logic (PATL) with causal-responsibility modalities, yielding PATL+R. The authors develop a parametric model of stochastic games with memoryless strategies and tolerate bounded-time properties, enabling model checking in $PSPACE$ and synthesis of Nash equilibrium strategies that balance expected rewards with responsibility degrees via CAR and CPR operators. They define utility functions that combine payoff and responsibility, and present a parametric model-checking-based approach to compute best responses and NE joint plans satisfying PATL+R properties. The approach supports fair distribution of responsibility and rewards in autonomous multi-agent settings and lays a foundation for responsibility-aware planning with formal guarantees, with potential for extensions to memoryful strategies and more expressive logics such as PSL.
Abstract
Responsibility plays a key role in the development and deployment of trustworthy autonomous systems. In this paper, we focus on the problem of strategic reasoning in probabilistic multi-agent systems with responsibility-aware agents. We introduce the logic PATL+R, a variant of Probabilistic Alternating-time Temporal Logic. The novelty of PATL+R lies in its incorporation of modalities for causal responsibility, providing a framework for responsibility-aware multi-agent strategic reasoning. We present an approach to synthesise joint strategies that satisfy an outcome specified in PATL+R, while optimising the share of expected causal responsibility and reward. This provides a notion of balanced distribution of responsibility and reward gain among agents. To this end, we utilise the Nash equilibrium as the solution concept for our strategic reasoning problem and demonstrate how to compute responsibility-aware Nash equilibrium strategies via a reduction to parametric model checking of concurrent stochastic multi-player games.