# Optimizing Delegation in Collaborative Human-AI Hybrid Teams arXiv: https://arxiv.org/abs/2402.05605 ยท HTML: https://mlsanity.com/paper/2402.05605/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 69 references that are in the map. - First posted on arXiv 8 Feb 2024. ## Builds on (most cited first, 30 of 30) - [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017) - [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018) - [Prioritized Experience Replay](https://mlsanity.com/paper/1511.05952/) (2015) - [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019) - [Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving](https://mlsanity.com/paper/1907.06826/) (2019) - [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021) - [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021) - [Adversarial Examples that Fool both Computer Vision and Time-Limited Humans](https://mlsanity.com/paper/1802.08195/) (2018) - [An Analysis of Adversarial Attacks and Defenses on Autonomous Driving Models](https://mlsanity.com/paper/2002.02175/) (2020) - [Triggering Failures: Out-Of-Distribution detection by learning from local adversarial attacks in Semantic Segmentation](https://mlsanity.com/paper/2108.01634/) (2021) - [Discovering and Validating AI Errors With Crowdsourced Failure Reports](https://mlsanity.com/paper/2109.11690/) (2021) - [Reinforcement Learning based Control of Imitative Policies for Near-Accident Driving](https://mlsanity.com/paper/2007.00178/) (2020) - [Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Processes](https://mlsanity.com/paper/1709.06656/) (2017) - [Automated Evaluation of Semantic Segmentation Robustness for Autonomous Driving](https://mlsanity.com/paper/1810.10193/) (2018) - [Option-Critic in Cooperative Multi-agent Systems](https://mlsanity.com/paper/1911.12825/) (2019) - [Collective Intelligence in Human-AI Teams: A Bayesian Theory of Mind Approach](https://mlsanity.com/paper/2208.11660/) (2022) - [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) - [Failure Prediction for Autonomous Driving](https://mlsanity.com/paper/1805.01811/) (2018) - [Did You Miss the Sign? A False Negative Alarm System for Traffic Sign Detectors](https://mlsanity.com/paper/1903.06391/) (2019) - [Learning to Make Adherence-Aware Advice](https://mlsanity.com/paper/2310.00817/) (2023) - [Modeling Trust in Human-Robot Interaction: A Survey](https://mlsanity.com/paper/2011.04796/) (2020) - [Decentralized Cooperative Planning for Automated Vehicles with Hierarchical Monte Carlo Tree Search](https://mlsanity.com/paper/1807.09530/) (2018) - [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023) - [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) - [Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2210.11237/) (2022) - [Reinforcement Learning Interventions on Boundedly Rational Human Agents in Frictionful Tasks](https://mlsanity.com/paper/2401.14923/) (2024) ## Built on by (most cited first, 3 of 3) - [Intelligent AI Delegation](https://mlsanity.com/paper/2602.11865/) (2026) - [Optimizing Risk-averse Human-AI Hybrid Teams](https://mlsanity.com/paper/2403.08386/) (2024) - [When Should a Human Take Back Control? Optimal Delegation under Turbulent AI Risk](https://mlsanity.com/paper/2609.32083/) (2026) ## Abstract (arXiv) When humans and autonomous systems operate together as what we refer to as a hybrid team, we of course wish to ensure the team operates successfully and effectively. We refer to team members as agents. In our proposed framework, we address the case of hybrid teams in which, at any time, only one team member (the control agent) is authorized to act as control for the team. To determine the best selection of a control agent, we propose the addition of an AI manager (via Reinforcement Learning) which learns as an outside observer of the team. The manager learns a model of behavior linking observations of agent performance and the environment/world the team is operating in, and from these observations makes the most desirable selection of a control agent. We restrict the manager task by introducing a set of constraints. The manager constraints indicate acceptable team operation, so a violation occurs if the team enters a condition which is unacceptable and requires manager intervention. To ensure minimal added complexity or potential inefficiency for the team, the manager should attempt to minimize the number of times the team reaches a constraint violation and requires subsequent manager intervention. Therefore our manager is optimizing its selection of authorized agents to boost overall team performance while minimizing the frequency of manager intervention. We demonstrate our manager performance in a simulated driving scenario representing the case of a hybrid team of agents composed of a human driver and autonomous driving system. We perform experiments for our driving scenario with interfering vehicles, indicating the need for collision avoidance and proper speed control. Our results indicate a positive impact of our manager, with some cases resulting in increased team performance up to ~187% that of the best solo agent performance.