Collaborative Decision Making Using Action Suggestions

TL;DR

This paper presents a collaborative decision-making method using action suggestions that enhances autonomous system performance and robustness without taking control, by efficiently integrating suggestions as environmental observations.

Contribution

It introduces a novel approach for using action suggestions in collaborative decision-making, improving performance with fewer suggestions and robustness to suboptimal inputs.

Findings

Achieves better action selection with fewer suggestions

Increases system performance and robustness

Demonstrated effectiveness through simulated experiments

Abstract

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.