# Too many cooks: Bayesian inference for coordinating multi-agent collaboration

arXiv: https://arxiv.org/abs/2003.11778 · HTML: https://mlsanity.com/paper/2003.11778/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2020.

## Built on by (most cited first, 29 of 29)

- [Collaborating with Humans without Human Data](https://mlsanity.com/paper/2110.08176/) (2021)
- [Scalable Evaluation of Multi-Agent Reinforcement Learning with Melting Pot](https://mlsanity.com/paper/2107.06857/) (2021)
- [Bridging the Novice-Expert Gap via Models of Decision-Making: A Case Study on Remediating Math Mistakes](https://mlsanity.com/paper/2310.10648/) (2023)
- [Welfare Diplomacy: Benchmarking Language Model Cooperation](https://mlsanity.com/paper/2310.08901/) (2023)
- [Melting Pot 2.0](https://mlsanity.com/paper/2211.13746/) (2022)
- [Autonomous Reinforcement Learning: Formalism and Benchmarking](https://mlsanity.com/paper/2112.09605/) (2021)
- [On the Critical Role of Conventions in Adaptive Human-AI Collaboration](https://mlsanity.com/paper/2104.02871/) (2021)
- [Evaluating the Robustness of Collaborative Agents](https://mlsanity.com/paper/2101.05507/) (2021)
- [Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination](https://mlsanity.com/paper/2201.12436/) (2022)
- [Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions](https://mlsanity.com/paper/2404.11023/) (2024)
- [TiZero: Mastering Multi-Agent Football with Curriculum Learning and Self-Play](https://mlsanity.com/paper/2302.07515/) (2023)
- [Expected Value of Communication for Planning in Ad Hoc Teamwork](https://mlsanity.com/paper/2103.01171/) (2021)
- [Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2209.10113/) (2022)
- [Generating Teammates for Training Robust Ad Hoc Teamwork Agents via Best-Response Diversity](https://mlsanity.com/paper/2207.14138/) (2022)
- [Learning to communicate about shared procedural abstractions](https://mlsanity.com/paper/2107.00077/) (2021)
- [Diverse Conventions for Human-AI Collaboration](https://mlsanity.com/paper/2310.15414/) (2023)
- [Conditional Imitation Learning for Multi-Agent Games](https://mlsanity.com/paper/2201.01448/) (2022)
- [A Review on Machine Theory of Mind](https://mlsanity.com/paper/2303.11594/) (2023)
- [Learning Altruistic Behaviours in Reinforcement Learning without External Rewards](https://mlsanity.com/paper/2107.09598/) (2021)
- [Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration](https://mlsanity.com/paper/2211.01602/) (2022)
- [LLM-Hanabi: Evaluating Multi-Agent Gameplays with Theory-of-Mind and Rationale Inference in Imperfect Information Collaboration Game](https://mlsanity.com/paper/2510.04980/) (2025)
- [Modeling Communication to Coordinate Perspectives in Cooperation](https://mlsanity.com/paper/2106.02164/) (2021)
- [A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents](https://mlsanity.com/paper/2204.02889/) (2022)
- [AI Must not be Fully Autonomous](https://mlsanity.com/paper/2507.23330/) (2025)
- [MetaMind: General and Cognitive World Models in Multi-Agent Systems by Meta-Theory of Mind](https://mlsanity.com/paper/2603.00808/) (2026)
- [Implicitly Aligning Humans and Autonomous Agents through Shared Task Abstractions](https://mlsanity.com/paper/2505.04579/) (2025)
- [Modeling Human Behavior Part I -- Learning and Belief Approaches](https://mlsanity.com/paper/2205.06485/) (2022)
- [Laser Learning Environment: A new environment for coordination-critical multi-agent tasks](https://mlsanity.com/paper/2404.03596/) (2024)
- [Generalized Intention Modeling in Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2605.31318/) (2026)

## Abstract (arXiv)

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating to solve a single task together and other times dividing it up into sub-tasks to work on in parallel. Underlying the human ability to collaborate is theory-of-mind, the ability to infer the hidden mental states that drive others to act. Here, we develop Bayesian Delegation, a decentralized multi-agent learning mechanism with these abilities. Bayesian Delegation enables agents to rapidly infer the hidden intentions of others by inverse planning. We test Bayesian Delegation in a suite of multi-agent Markov decision processes inspired by cooking problems. On these tasks, agents with Bayesian Delegation coordinate both their high-level plans (e.g. what sub-task they should work on) and their low-level actions (e.g. avoiding getting in each other's way). In a self-play evaluation, Bayesian Delegation outperforms alternative algorithms. Bayesian Delegation is also a capable ad-hoc collaborator and successfully coordinates with other agent types even in the absence of prior experience. Finally, in a behavioral experiment, we show that Bayesian Delegation makes inferences similar to human observers about the intent of others. Together, these results demonstrate the power of Bayesian Delegation for decentralized multi-agent collaboration.