# Optimizing delegation between human and AI collaborative agents

arXiv: https://arxiv.org/abs/2309.14718 · HTML: https://mlsanity.com/paper/2309.14718/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 11 of 11)

- [Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes](https://mlsanity.com/paper/1709.06656/) (2017)
- [Option-Critic in Cooperative Multi-agent Systems](https://mlsanity.com/paper/1911.12825/) (2019)
- [Reinforcement Learning Under Algorithmic Triage](https://mlsanity.com/paper/2109.11328/) (2021)
- [LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2205.02561/) (2022)
- [Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors](https://mlsanity.com/paper/1903.06391/) (2019)
- [Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search](https://mlsanity.com/paper/1807.09530/) (2018)
- [A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents](https://mlsanity.com/paper/2204.02889/) (2022)
- [Developing cooperative policies for multi-stage reinforcement learning tasks](https://mlsanity.com/paper/2205.05230/) (2022)
- [Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems](https://mlsanity.com/paper/2303.01300/) (2023)
- [Learning Reusable Options for Multi-Task Reinforcement Learning](https://mlsanity.com/paper/2001.01577/) (2020)
- [Hierarchical Decompositions of Stochastic Pursuit-Evasion Games](https://mlsanity.com/paper/2205.00885/) (2022)

## Built on by (most cited first, 9 of 9)

- [Towards Human-AI Complementarity with Prediction Sets](https://mlsanity.com/paper/2405.17544/) (2024)
- [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026)
- [Controlling Counterfactual Harm in Decision Support Systems Based on Prediction Sets](https://mlsanity.com/paper/2406.06671/) (2024)
- [When Should We Orchestrate Multiple Agents?](https://mlsanity.com/paper/2503.13577/) (2025)
- [Towards Human-AI Complementarity in Matching Tasks](https://mlsanity.com/paper/2508.13285/) (2025)
- [Securing Agentic AI: Threat Modeling and Risk Analysis for Network Monitoring Agentic AI System](https://mlsanity.com/paper/2508.10043/) (2025)
- [Optimizing Delegation in Collaborative Human-AI Hybrid Teams](https://mlsanity.com/paper/2402.05605/) (2024)
- [Optimizing Risk-averse Human-AI Hybrid Teams](https://mlsanity.com/paper/2403.08386/) (2024)
- [Agentic AI Autonomy Assessment: A Decision-Support Framework Towards Governed Supply Chain Systems](https://mlsanity.com/paper/2607.25405/) (2026)

## Abstract (arXiv)

In the context of humans operating with artificial or autonomous agents in a hybrid team, it is essential to accurately identify when to authorize those team members to perform actions. Given past examples where humans and autonomous systems can either succeed or fail at tasks, we seek to train a delegating manager agent to make delegation decisions with respect to these potential performance deficiencies. Additionally, we cannot always expect the various agents to operate within the same underlying model of the environment. It is possible to encounter cases where the actions and transitions would vary between agents. Therefore, our framework provides a manager model which learns through observations of team performance without restricting agents to matching dynamics. Our results show our manager learns to perform delegation decisions with teams of agents operating under differing representations of the environment, significantly outperforming alternative methods to manage the team.