Summary of Optimizing Delegation in Collaborative Human-ai Hybrid Teams, by Andrew Fuchs et al.
Optimizing Delegation in Collaborative Human-AI Hybrid Teams
by Andrew Fuchs, Andrea Passarella, Marco Conti
First submitted to arxiv on: 8 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework addresses the challenge of ensuring successful hybrid teams consisting of humans and autonomous systems. In this scenario, only one team member (the control agent) is authorized to act as control for the team at any given time. To determine the optimal selection of a control agent, an AI manager is introduced via Reinforcement Learning. The manager learns a model of behavior linking observations of agent performance and environmental conditions, allowing it to make informed decisions about selecting the most desirable control agent. By introducing constraints that define acceptable team operation, the manager minimizes constraint violations and optimizes overall team performance while reducing the frequency of intervention. Experimental results in a simulated driving scenario demonstrate a positive impact on team performance, with some cases achieving up to 187% improvement over solo agent performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers develop a way for humans and machines to work together effectively as a team. They focus on finding the best person (or machine) to be in charge of the team at any given time. The AI manager uses machine learning to learn what makes good decisions about who should be in charge and when. It does this by watching how well different agents perform and trying to find patterns that link their actions to the environment they’re working in. The AI manager also has rules about what’s acceptable behavior for the team, so it can step in if something goes wrong. The researchers tested this system in a driving simulation where there are other cars on the road that might cause problems. They found that having an AI manager helped the team perform better than when just one agent was in charge. |
Keywords
* Artificial intelligence * Machine learning * Reinforcement learning
