# Automated Task-Time Interventions to Improve Teamwork using Imitation Learning

arXiv: https://arxiv.org/abs/2303.00413 · HTML: https://mlsanity.com/paper/2303.00413/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 13 of its 50 references that are in the map.
- First posted on arXiv 1 Mar 2023.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 13 of 13)

- [Learning to Communicate with Deep Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1605.06676/) (2016)
- [An Algorithmic Perspective on Imitation Learning](https://mlsanity.com/paper/1811.06711/) (2018)
- [Learning Attentional Communication for Multi-Agent Cooperation](https://mlsanity.com/paper/1805.07733/) (2018)
- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [Multi-Agent Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1807.09936/) (2018)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [A Survey of Ad Hoc Teamwork Research](https://mlsanity.com/paper/2202.10450/) (2022)
- [Directed-Info GAIL: Learning Hierarchical Policies from Unsegmented Demonstrations using Directed Information](https://mlsanity.com/paper/1810.01266/) (2018)
- [Game Plan: What AI can do for Football, and What Football can do for AI](https://mlsanity.com/paper/2011.09192/) (2020)
- [Adversarial Option-Aware Hierarchical Imitation Learning](https://mlsanity.com/paper/2106.05530/) (2021)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [Towards an AI Coach to Infer Team Mental Model Alignment in Healthcare](https://mlsanity.com/paper/2102.08507/) (2021)
- [Semi-Supervised Imitation Learning of Team Policies from Suboptimal Demonstrations](https://mlsanity.com/paper/2205.02959/) (2022)

## Built on by (most cited first, 7 of 7)

- [IDIL: Imitation Learning of Intent-Driven Expert Behavior](https://mlsanity.com/paper/2404.16989/) (2024)
- [Boosting Team Modeling through Tempo-Relational Representation Learning](https://mlsanity.com/paper/2507.13305/) (2025)
- [Hierarchical Imitation Learning of Team Behavior from Heterogeneous Demonstrations](https://mlsanity.com/paper/2502.17618/) (2025)
- [Socratic: Enhancing Human Teamwork via AI-enabled Coaching](https://mlsanity.com/paper/2502.17643/) (2025)
- [ProToM: Promoting Prosocial Behaviour via Theory of Mind-Informed Feedback](https://mlsanity.com/paper/2509.05091/) (2025)
- [MindZero: Learning Online Mental Reasoning With Zero Annotations](https://mlsanity.com/paper/2606.00240/) (2026)
- [From Multimodal Observation to Interpretable Suggestions: Counterfactual Time-Expanded Relational Modeling of Surgical Teams](https://mlsanity.com/paper/2608.23254/) (2026)

## Abstract (arXiv)

Effective human-human and human-autonomy teamwork is critical but often challenging to perfect. The challenge is particularly relevant in time-critical domains, such as healthcare and disaster response, where the time pressures can make coordination increasingly difficult to achieve and the consequences of imperfect coordination can be severe. To improve teamwork in these and other domains, we present TIC: an automated intervention approach for improving coordination between team members. Using BTIL, a multi-agent imitation learning algorithm, our approach first learns a generative model of team behavior from past task execution data. Next, it utilizes the learned generative model and team's task objective (shared reward) to algorithmically generate execution-time interventions. We evaluate our approach in synthetic multi-agent teaming scenarios, where team members make decentralized decisions without full observability of the environment. The experiments demonstrate that the automated interventions can successfully improve team performance and shed light on the design of autonomous agents for improving teamwork.