Strategic Decision Support for AI Agents
Quick Answer
This paper proposes a framework for strategic decision support in AI agents, focusing on minimizing support usage while controlling missed-support errors.
Quick Take
An online algorithm is developed to adaptively manage support thresholds, demonstrating effectiveness across various scenarios like human-AI collaboration and information gathering, ultimately reducing unnecessary support calls and maintaining alignment with human goals.
Key Points
- Framework minimizes support usage while controlling counterfactual missed-support errors.
- Optimal policy is a threshold rule based on the value of support.
- Online algorithm uses randomized exploration to manage support thresholds.
- Calibration-on-the-fly method reduces unnecessary support calls.
- Experiments show reliable error control and significant support reduction.
Paper Resources
Source Excerpt
arXiv:2606. 12587v1 Announce Type: new Abstract: Traditionally, decision support studies how humans use machine learning models to make better decisions. In modern agentic systems, this division of roles is increasingly reversed: AI agents act on behalf of users, while humans and tools becomes support mechanisms around them. This role reversal brings reliability concerns to the forefront, since agentic errors can be consequential and agent behavior must remain aligned with human goals and constraints. …
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