# Balancing Performance and Human Autonomy with Implicit Guidance Agent

arXiv: https://arxiv.org/abs/2109.00414 · HTML: https://mlsanity.com/paper/2109.00414/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 53 references that are in the map.
- First posted on arXiv 1 Sep 2021.

## Builds on (most cited first, 12 of 12)

- [On the Utility of Learning about Humans for Human-AI Coordination](https://mlsanity.com/paper/1910.05789/) (2019)
- [Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1606.03137/) (2016)
- [Inverse Reward Design](https://mlsanity.com/paper/1711.02827/) (2017)
- [Maximum Entropy Deep Inverse Reinforcement Learning](https://mlsanity.com/paper/1507.04888/) (2015)
- [Online Bayesian Goal Inference for Boundedly-Rational Planning Agents](https://mlsanity.com/paper/2006.07532/) (2020)
- [AvE: Assistance via Empowerment](https://mlsanity.com/paper/2006.14796/) (2020)
- [Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration](https://mlsanity.com/paper/1701.07790/) (2017)
- [An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning](https://mlsanity.com/paper/1806.03820/) (2018)
- [Learning to Share and Hide Intentions using Information Regularization](https://mlsanity.com/paper/1808.02093/) (2018)
- [Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning](https://mlsanity.com/paper/1810.08647/) (2018)
- [Robot Planning with Mathematical Models of Human State and Action](https://mlsanity.com/paper/1705.04226/) (2017)
- [Modeling Human Inference of Others' Intentions in Complex Situations with Plan Predictability Bias](https://mlsanity.com/paper/1805.06248/) (2018)

## Abstract (arXiv)

The human-agent team, which is a problem in which humans and autonomous agents collaborate to achieve one task, is typical in human-AI collaboration. For effective collaboration, humans want to have an effective plan, but in realistic situations, they might have difficulty calculating the best plan due to cognitive limitations. In this case, guidance from an agent that has many computational resources may be useful. However, if an agent guides the human behavior explicitly, the human may feel that they have lost autonomy and are being controlled by the agent. We therefore investigated implicit guidance offered by means of an agent's behavior. With this type of guidance, the agent acts in a way that makes it easy for the human to find an effective plan for a collaborative task, and the human can then improve the plan. Since the human improves their plan voluntarily, he or she maintains autonomy. We modeled a collaborative agent with implicit guidance by integrating the Bayesian Theory of Mind into existing collaborative-planning algorithms and demonstrated through a behavioral experiment that implicit guidance is effective for enabling humans to maintain a balance between improving their plans and retaining autonomy.