# Collaborative Decision Making Using Action Suggestions

arXiv: https://arxiv.org/abs/2209.13160 · HTML: https://mlsanity.com/paper/2209.13160/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 44 references that are in the map.
- First posted on arXiv 27 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Advances in adversarial attacks and defenses in computer vision: A survey](https://mlsanity.com/paper/2108.00401/) (2021)
- [Shared Autonomy via Deep Reinforcement Learning](https://mlsanity.com/paper/1802.01744/) (2018)
- [Shared Autonomy via Hindsight Optimization](https://mlsanity.com/paper/1503.07619/) (2015)
- [Shared Autonomy via Hindsight Optimization for Teleoperation and Teaming](https://mlsanity.com/paper/1706.00155/) (2017)
- [When Humans Aren't Optimal: Robots that Collaborate with Risk-Aware Humans](https://mlsanity.com/paper/2001.04377/) (2020)
- [Shared Autonomy with Learned Latent Actions](https://mlsanity.com/paper/2005.03210/) (2020)
- [Corrective Shared Autonomy for Addressing Task Variability](https://mlsanity.com/paper/2102.07165/) (2021)

## Built on by (most cited first, 2 of 2)

- [Leveraging Counterfactual Paths for Contrastive Explanations of POMDP Policies](https://mlsanity.com/paper/2403.19760/) (2024)
- [Learning to Trust: Bayesian Adaptation to Varying Suggester Reliability in Sequential Decision Making](https://mlsanity.com/paper/2511.12378/) (2025)

## Abstract (arXiv)

The level of autonomy is increasing in systems spanning multiple domains, but these systems still experience failures. One way to mitigate the risk of failures is to integrate human oversight of the autonomous systems and rely on the human to take control when the autonomy fails. In this work, we formulate a method of collaborative decision making through action suggestions that improves action selection without taking control of the system. Our approach uses each suggestion efficiently by incorporating the implicit information shared through suggestions to modify the agent's belief and achieves better performance with fewer suggestions than naively following the suggested actions. We assume collaborative agents share the same objective and communicate through valid actions. By assuming the suggested action is dependent only on the state, we can incorporate the suggested action as an independent observation of the environment. The assumption of a collaborative environment enables us to use the agent's policy to estimate the distribution over action suggestions. We propose two methods that use suggested actions and demonstrate the approach through simulated experiments. The proposed methodology results in increased performance while also being robust to suboptimal suggestions.