# Assisting Unknown Teammates in Unknown Tasks: Ad Hoc Teamwork under Partial Observability

arXiv: https://arxiv.org/abs/2201.03538 · HTML: https://mlsanity.com/paper/2201.03538/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 10 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- ["Other-Play" for Zero-Shot Coordination](https://mlsanity.com/paper/2003.02979/) (2020)
- [A New Formalism, Method and Open Issues for Zero-Shot Coordination](https://mlsanity.com/paper/2106.06613/) (2021)
- [Quasi-Equivalence Discovery for Zero-Shot Emergent Communication](https://mlsanity.com/paper/2103.08067/) (2021)

## Built on by (most cited first, 6 of 6)

- [A Survey of Ad Hoc Teamwork Research](https://mlsanity.com/paper/2202.10450/) (2022)
- [PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination](https://mlsanity.com/paper/2301.06387/) (2023)
- [A General Learning Framework for Open Ad Hoc Teamwork Using Graph-based Policy Learning](https://mlsanity.com/paper/2210.05448/) (2022)
- [Optimal Behavior Prior: Data-Efficient Human Models for Improved Human-AI Collaboration](https://mlsanity.com/paper/2211.01602/) (2022)
- [Zero-Shot Coordination in Ad Hoc Teams with Generalized Policy Improvement and Difference Rewards](https://mlsanity.com/paper/2510.16187/) (2025)
- [Multi-Agent Inverse Q-Learning from Demonstrations](https://mlsanity.com/paper/2503.04679/) (2025)

## Abstract (arXiv)

In this paper, we present a novel Bayesian online prediction algorithm for the problem setting of ad hoc teamwork under partial observability (ATPO), which enables on-the-fly collaboration with unknown teammates performing an unknown task without needing a pre-coordination protocol. Unlike previous works that assume a fully observable state of the environment, ATPO accommodates partial observability, using the agent's observations to identify which task is being performed by the teammates. Our approach assumes neither that the teammate's actions are visible nor an environment reward signal. We evaluate ATPO in three domains -- two modified versions of the Pursuit domain with partial observability and the overcooked domain. Our results show that ATPO is effective and robust in identifying the teammate's task from a large library of possible tasks, efficient at solving it in near-optimal time, and scalable in adapting to increasingly larger problem sizes.