# A Cognitive Framework for Delegation Between Error-Prone AI and Human Agents

arXiv: https://arxiv.org/abs/2204.02889 · HTML: https://mlsanity.com/paper/2204.02889/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 39 references that are in the map.
- First posted on arXiv 6 Apr 2022.

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

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Core Challenges of Social Robot Navigation: A Survey](https://mlsanity.com/paper/2103.05668/) (2021)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Theory of Minds: Understanding Behavior in Groups Through Inverse Planning](https://mlsanity.com/paper/1901.06085/) (2019)
- [Too many cooks: Bayesian inference for coordinating multi-agent collaboration](https://mlsanity.com/paper/2003.11778/) (2020)
- [Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness](https://mlsanity.com/paper/2003.01090/) (2020)
- [Human-robot collaboration and machine learning: a systematic review of recent research](https://mlsanity.com/paper/2110.07448/) (2021)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Theory of Mind for Deep Reinforcement Learning in Hanabi](https://mlsanity.com/paper/2101.09328/) (2021)
- [How to Support Users in Understanding Intelligent Systems? Structuring the Discussion](https://mlsanity.com/paper/2001.08301/) (2020)
- [Unpacking Human Teachers' Intentions For Natural Interactive Task Learning](https://mlsanity.com/paper/2102.06755/) (2021)

## Built on by (most cited first, 5 of 5)

- [Optimizing delegation between human and AI collaborative agents](https://mlsanity.com/paper/2309.14718/) (2023)
- [Compensating for Sensing Failures via Delegation in Human-AI Hybrid Systems](https://mlsanity.com/paper/2303.01300/) (2023)
- [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)
- [Bayesian Inference for Correlated Human Experts and Classifiers](https://mlsanity.com/paper/2506.05636/) (2025)

## Abstract (arXiv)

With humans interacting with AI-based systems at an increasing rate, it is necessary to ensure the artificial systems are acting in a manner which reflects understanding of the human. In the case of humans and artificial AI agents operating in the same environment, we note the significance of comprehension and response to the actions or capabilities of a human from an agent's perspective, as well as the possibility to delegate decisions either to humans or to agents, depending on who is deemed more suitable at a certain point in time. Such capabilities will ensure an improved responsiveness and utility of the entire human-AI system. To that end, we investigate the use of cognitively inspired models of behavior to predict the behavior of both human and AI agents. The predicted behavior, and associated performance with respect to a certain goal, is used to delegate control between humans and AI agents through the use of an intermediary entity. As we demonstrate, this allows overcoming potential shortcomings of either humans or agents in the pursuit of a goal.