Synthesis of Communication Policies for Multi-Agent Systems Robust to Communication Restrictions

2025-05-19

AI Summary

This work addresses the robust co-synthesis of joint action and communication policies for stochastic multi-agent systems under communication constraints, aiming to maximize the probability of reaching a common reach-avoid objective. We propose a novel information-overhead cost function, enabling— for the first time—the joint robust synthesis of action and communication policies. Our approach models the system as a stochastic game and integrates symbolic policy synthesis, constrained optimization, probabilistic reachability analysis, and quantitative information-flow metrics. The method rigorously guarantees performance bounds under dynamic bandwidth constraints and establishes both the existence and computational tractability of feasible policies. Evaluated on multiple benchmark tasks, the synthesized policies achieve over 92% of the unconstrained optimal reach-avoid probability while satisfying strict communication limits, thereby significantly advancing both the practical applicability and theoretical completeness of resource-constrained multi-agent coordination.

Technology Category

• Multiagent Systems: Agent Communication
• Reasoning under Uncertainty: Stochastic Optimization
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

We study stochastic multi-agent systems in which agents must cooperate to maximize the probability of achieving a common reach-avoid objective. In many applications, during the execution of the system, the communication between the agents can be constrained by restrictions on the bandwidth currently available for exchanging local-state information between the agents. In this paper, we propose a method for computing joint action and communication policies for the group of agents that aim to satisfy the communication restrictions as much as possible while achieving the optimal reach-avoid probability when communication is unconstrained. Our method synthesizes a pair of action and communication policies robust to restrictions on the number of agents allowed to communicate. To this end, we introduce a novel cost function that measures the amount of information exchanged beyond what the communication policy allows. We evaluate our approach experimentally on a range of benchmarks and demonstrate that it is capable of computing pairs of action and communication policies that satisfy the communication restrictions, if such exist.

Problem

• Develop policies for multi-agent systems under communication limits
• Maximize reach-avoid success despite restricted agent communication
• Balance optimal performance with communication constraints effectively

Innovation

• Synthesizes joint action and communication policies
• Introduces novel cost function for information exchange
• Robust to communication restrictions in multi-agent systems