# Collaborative Decision Making Using Action Suggestions Source: [Collaborative Decision Making Using Action Suggestions]() Collection index: [llms.txt]() Data updated: 2026-01-02T13:13:14.639Z Authors: Dylan M. Asmar, Mykel J. Kochenderfer Published: 2022-09-27T05:16:41.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [POMDP Simulation on Tag](): POMDP Simulation; Tag; Reward: -1.8. - [POMDP Simulation on RS (7, 8, 20, 0)](): POMDP Simulation; RS (7, 8, 20, 0); Reward: 27.8. - [POMDP Simulation on RS(8, 4, 10, -1)](): POMDP Simulation; RS(8, 4, 10, -1); Reward: 16.4. - [Sequential Decision Making on Tag](): Sequential Decision Making; Tag; Average Reward: -2.8. - [Sequential Decision Making on RockSample (7, 8, 20, 0)](): Sequential Decision Making; RockSample (7, 8, 20, 0); Average Reward: 27.9. - [Sequential Decision Making on RockSample (8, 4, 10, -1)](): Sequential Decision Making; RockSample (8, 4, 10, -1); Average Reward: 16.5. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()