Formal Verification of Learned Multi-Agent Communication Policies via Decision Tree Distillation
arXiv:2606.19632v1 Announce Type: cross Abstract: Multi-agent reinforcement learning (MARL) enables agents to develop coordination strategies through emergent communication, but neural policies lack the formal safety guarantees required for safety-critical robotic deployment in drone swarms and autonomous vehicle fleets. We present the first end-to-end framework for safety verification of learned multi-agent communication policies through policy abstraction: neural policies are distilled into interpretable decision trees, then formally verified, with empirical validation confirming that verifi
The increasing complexity and deployment of multi-agent AI systems in critical applications necessitate robust safety and reliability guarantees, coinciding with advancements in formal methods for AI.
Ensuring the safety of learned multi-agent communication policies is a prerequisite for widespread adoption in domains like autonomous vehicles and drone swarms, directly impacting public trust and feasibility.
This framework offers a pathway to formally verify multi-agent AI, moving beyond empirical testing towards provable safety for complex, emergent coordination strategies.
- · Robotics companies
- · Autonomous vehicle developers
- · Defence sector
- · AI safety researchers
- · Developers of unverified AI systems
- · Traditional black-box neural network approaches
Safety-critical robotic applications can begin to integrate complex multi-agent AI with higher confidence.
New regulatory frameworks may emerge, requiring formal verification for AI systems deployed in high-stakes environments.
The development of formal verification tools could become a specialized and highly valued sub-industry within AI.
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Read at arXiv cs.LG