AI & Optimization

Research Areas

  • Reinforcement learning
  • Agent-Based modeling
  • Protocol simulation
  • Automated negotiations
  • Optimal pricing


Ethereum and other blockchains support decentralized, permissionless, programs called smart contracts. Smart contracts are combined with off-chain programs to build Web3 protocols. These protocols often have various types of roles, with each role possessing unique responsibilities, rights, and incentives. While real people are normally responsible for fulfilling the requirements of a protocol role, there are often too many factors to take into account for a person to perform irrationally. Furthermore, it is difficult for a protocol designer to predict what will happen when optimal (and perhaps malicious) actors engage with their system. Semiotic Labs uses reinforcement learning and related AI techniques to eliminate these problems by providing simulation and automation tools to protocol designers and human users. We improve the efficiency and security of people and Web3 protocols

Current work

Our AI work is focused on improving The Graph protocol. A recent milestone for that effort was the development of what could be the world’s first reinforcement learning agent to autonomously choose prices and compete in a Web3 market. Simulation is a foundation for advanced results in AI. Most of our current efforts are focused on building a simulation capability that both meets the needs of The Graph’s developers and serves as a platform for training more autonomous agents within The Graph. In addition to our work in The Graph, we organize the Workshop on Incentive Mechanism Validation, which brings researchers and protocol developers together to discuss the application of AI for building and improving Web3 protocols.

AI & Optimization Team

Tomasz Kornuta, Ph.D.

VP of Engineering / Head of AI

Sam Green, Ph.D.

Head of Research & Co-founder

Anirudh Patel

Senior Research Scientist

Matt Deible

Research Scientist


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