Learning Provably Correct Distributed Protocols Without Human Knowledge

2026-01-29

AI Summary

Designing provably correct distributed protocols is highly challenging, and traditional approaches rely heavily on manual effort and are inefficient. This work proposes an automated protocol synthesis framework that formulates the problem as strategy search in a game of imperfect information, integrating Monte Carlo tree search, a Transformer-based action encoder, global depth-first search, and SMT-driven model checking. The method is the first to automatically generate protocols that satisfy formal specifications without requiring human-provided prior knowledge, guarantees search completeness under mild assumptions, and scales to larger systems than existing techniques. Its effectiveness is validated through rigorous model checking.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Multiagent Systems: Mechanism Design
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Provably correct distributed protocols, which are a critical component of modern distributed systems, are highly challenging to design and have often required decades of human effort. These protocols allow multiple agents to coordinate to come to a common agreement in an environment with uncertainty and failures. We formulate protocol design as a search problem over strategies in a game with imperfect information, and the desired correctness conditions are specified in Satisfiability Modulo Theories (SMT). However, standard methods for solving multi-agent games fail to learn correct protocols in this setting, even when the number of agents is small. We propose a learning framework, GGMS, which integrates a specialized variant of Monte Carlo Tree Search with a transformer-based action encoder, a global depth-first search to break out of local minima, and repeated feedback from a model checker. Protocols output by GGMS are verified correct via exhaustive model checking for all executions within the bounded setting. We further prove that, under mild assumptions, the search process is complete: if a correct protocol exists, GGMS will eventually find it. In experiments, we show that GGMS can learn correct protocols for larger settings than existing methods.

Problem

• distributed protocols
• provably correct
• multi-agent coordination
• imperfect information
• SMT

Innovation

• Monte Carlo Tree Search
• Transformer-based action encoder
• SMT-based specification
• Model checking
• Distributed protocol synthesis