AI Research

MIT, UChicago, Harvard, Diffeo Researchers Use Bayesian Inference to Cook Up Multi-Agent Collaboration

MIT, UChicago, Harvard and Diffeo *Bayesian Delegation* multi-agent learning mechanism enables agents to coordinate behaviour on the fly

“Figuring out how to flexibly coordinate a collaborative endeavour is a fundamental challenge for any agent in a multi-agent world,” explains a team of researchers from MIT, UChicago, Harvard and Diffeo. Their recipe for cooperation is Bayesian Delegation, a decentralized multi-agent learning mechanism that enables agents to coordinate their behaviour on the fly. Bayesian Delegation uses Bayesian inference with inverse planning to rapidly infer the sub-tasks other agents are working on, a probabilistic approach that enables agents to better predict others’ intentions despite uncertainty and ambiguity of behaviours.

The paper Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration points to theory-of-mind (ToM) capabilities that allow humans to understand intentions from actions and cooperate in coordinated ways, and proposes that Bayesian Delegation could equip AI agents with such abilities.

In their research, the team recreated the environment of Overcooked!, a popular co-op cooking simulation game where players control kitchen chefs who overcome various obstacles and hazards to prepare meal orders. Players must collaborate to deliver orders promptly, which makes the environment ideal for the study of real-time decision-making and strategy development in AI agents.


The team choose cooking as it offers many similar features to other object-oriented tasks such as construction and assembly. The sub-tasks such as chopping, plating, and delivering also allowed researchers to study agents under challenging coordination conditions:

  • Divide and conquer: agents should work in parallel when sub-tasks can be efficiently carried out individually
  • Cooperation: agents should work together on the same sub-task when most efficient or necessary
  • Spatio-temporal movement: agents should avoid getting in each other’s way at any time

While other deep reinforcement learning studies have used Overcooked!-inspired environments to train agents using self-play and human data, the new approach focus on techniques that agents dynamically learn while interacting with others. The researchers say the work shares goals with “ad-hoc coordination literature, where agents must adapt on the fly to variations in task, environment, or team.”


In evaluations in a suite of new multi-agent environments, Bayesian Delegation outperformed four baseline agent algorithms in a self-play evaluation. It also showed capabilities for ad-hoc collaboration and coordination with other agent types even without prior experience. And encouragingly, behavioural experiments suggested that Bayesian Delegation inferences about others’ intents are close to human judgements.

The team notes that as systems scale up their number of agents, “there can be ‘too many cooks’ in the kitchen!” and that efficiently coordinating the behaviour of larger groups of agents will be essential for partnering agents with human teams or other agents in such complex environments.

The paper Too Many Cooks: Bayesian Inference for Coordinating Multi-Agent Collaboration is on arXiv.

Reporter: Fangyu Cai | Editor: Michael Sarazen

AI Weekly.png

We know you don’t want to miss any news or research breakthroughs. Subscribe to our popular newsletter Synced Global AI Weekly to get weekly AI updates.

Thinking of contributing to Synced Review? Synced’s new column Share My Research welcomes scholars to share their own research breakthroughs with global AI enthusiasts.


37 comments on “MIT, UChicago, Harvard, Diffeo Researchers Use Bayesian Inference to Cook Up Multi-Agent Collaboration

  1. Thanks for you.

  2. Good article and thank you

  3. Thank you very much

  4. Good good article

  5. Very nice

  6. Thank’s and very nice

  7. Thank you for article is good

  8. Very very very gooooooooood.

  9. Good and thank,s

  10. Very very nice

  11. This article niiiiice

  12. Very good and nice….

  13. Mèrci pour article il bien

  14. Good article i like to

  15. Very very nice

  16. Thank you very much this topic is nice

  17. Topic is good

  18. Good goooooooood article

  19. Very very very nice

  20. Very very niiiiiice.

  21. Good article good.

  22. Mèrci mèrci mèrci

  23. Good good good good.

  24. Very very niiiiiice

  25. Very very very very niiiiiiiiiiiiiiiiiiiiiiiice

  26. Good and nice .

  27. Very

  28. Mèrci pour article bien

  29. Bien bien article

  30. Bien bien article

  31. Niiiiiice niiiiiice

  32. Mèrci mèrci est bien article

  33. Mèrci
    Bien article…..

  34. Good good article.

  35. Pingback: Top updates in AI during week 45 of 2020 - Ankitaism

Leave a Reply to Ziane Cancel reply

Your email address will not be published. Required fields are marked *

%d bloggers like this: