Register now to shape the future of AI cooperation!

The Concordia Contest is your opportunity to dive into the exciting world of cooperative AI. Whether you're an experienced AI researcher, a curious developer, or someone passionate about ensuring AI benefits humanity, this event is for you. As a participant, you will:

• Collaborate in a diverse team to create groundbreaking AI agents

• Learn from experts in AI safety, cooperative AI, and multi-agent systems, including Sasha Vezhnevets, Joel Leibo, Rakshit Trivedi, and Lewis Hammond

• Contribute to solving real-world challenges like resource management and conflict resolution

• Compete for prizes and the chance to be featured in a NeurIPS publication

• Network with like-minded individuals and potential future collaborators

Register now and be part of the movement towards more cooperative, trustworthy, and beneficial AI systems. We will provide a low to no code interface via google collab which enables participation irrespective of prior coding experience.

Here is a quick video tutorial to get you started

A competition in cooperative AI:

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As AI systems become more sophisticated and pervasive, we must develop agents capable of cooperating effectively with humans and other AIs. Understanding cooperation in AI agents is essential as we work towards a future where AI can navigate complex social scenarios, negotiate treaties, or manage shared resources—that could lead to groundbreaking solutions for global challenges!

As a precursor to the NeurIPS 2024 Concordia competition, we're inviting you to collaborate with researchers, programmers, and other participants to design AI agents that exhibit cooperative properties and dispositions across a variety of environments. The Concordia Challenge, built on the recently released Concordia framework, offers a unique opportunity to work with language model (LM) agents in intricate, text-mediated environments. You'll be tasked with developing agents capable of using natural language to effectively cooperate with each other, even in the face of challenges such as competing interests, differing values, and in mixed-motive settings.

Prize Pool: $2,000 (standard breakdown)

• 🥇 $1,000 for the top team

• 🥈 $600 for the second prize

• 🥉 $300 for the third prize

• 🏅 $100 for the fourth prize

Winning submissions may have the opportunity to collaborate and co-author a NeurIPS report -- in collaboration with authors from Cooperative AI Foundation, GoogleDeepMind, MIT, University of Washington, UC Berkeley, and University College London.

Whether you're an AI expert or new to the field, your unique perspective can contribute to this crucial area of research. The goal of this hackathon goes beyond winning prizes. Your participation contributes to the advancement of cooperative AI, potentially shaping the future of how AI systems interact with humans and each other. ‍Good luck to all participants, and we look forward to seeing your innovative solutions!

The Concordia Framework Explained:

This diagram illustrates how agents interact within the Concordia environment:

1. Agents (like Dorothy and Charlie) make action attempts in natural language.

2. The Game Master (GM) processes these attempts and determines outcomes.

3. The GM generates event statements describing what actually occurred.

4. These events update the world state and are sent back to agents as observations.

This cycle creates a dynamic, text-based environment where AI agents must cooperate and communicate to achieve goals, mimicking complex social interactions. Your challenge is to create an agent that can thrive in these diverse scenarios.

Designing Your Agent:

• Consider creating a backstory or set of experiences for your agent to inform its decision-making process.

• Explore various psychological profiles, personality tests, or historical figures as inspiration for your agent's behavior.

• Think about giving your agent a motivation or purpose, such as acting on behalf of a family or community.

• Experiment with different approaches, such as using social psychology research or creating fictional memories to shape your agent's cooperative tendencies.

• Remember that your agent should be able to adapt to various roles and scenarios, so avoid being too prescriptive in its behavior.

Collaboration and Development

• We encourage participants to build in public, especially during the first 24 hours of the hackathon.

• Share your ideas and get feedback from other participants to improve your agent's design.

• Consider forming diverse teams to tackle the challenge from multiple perspectives.

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Environments

• We have several different environments that proceed in simultaneous-move rounds, controlled by a “game master” (like a storyteller)

• Environments are mixed-motive, incomplete information, equilibrium selection problems that contain a “background population” of agents against which the participants are evaluated (similar to MeltingPot Contest)

• Some environments are extremely open-ended so agents can suggest almost any action to the game master (who then resolves the action, determining whether or not they can do it using common sense and environment knowledge); other environments contain mixtures of open-ended free response choices and multiple choice questions

• All environments track concrete variables (e.g. money) which we also use for scoring

• The environments we have so far are:
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  • Pub Coordination: A group of friends with individual pub preferences must coordinate their choices for a night out, balancing personal desires with group harmony while adapting to potential unexpected closures and engaging in social interactions.

  • Haggling: A fruit market in Fruitville where merchants engage in price negotiations, balancing potential profits against the risk of the co-player refusing the transaction.

  • Labor Collective Action: Workers must decide whether to strike or continue working in the face of wage cuts, navigating collective action problems and power dynamics with a boss and labor organizer.

  • Reality Show: Contestants participate in a series of minigames with varied game-theoretic structures, balancing cooperation and competition across different scenarios.

Environments are located here: https://github.com/google-deepmind/concordia/tree/main/examples/modular/environment