In Pursuit Of Predictive Models Of Human Preferences Toward AI Teammates

Abstract

We seek measurable properties of AI agents that make them better or worse teammates from the subjective perspective of human collaborators. Our experiments use the cooperative card game Hanabi -- a common benchmark for AI-teaming research. We first evaluate AI agents on a set of objective metrics based on task performance, information theory, and game theory, which are measurable without human interaction. Next, we evaluate subjective human preferences toward AI teammates in a large-scale (N=241) human-AI teaming experiment. Finally, we correlate the AI-only objective metrics with the human subjective preferences. Our results refute common assumptions from prior literature on reinforcement learning, revealing new correlations between AI behaviors and human preferences. We find that the final game score a human-AI team achieves is less predictive of human preferences than esoteric measures of AI action diversity, strategic dominance, and ability to team with other AI. In the future, the

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keysiu2025in

Related papers

• Evaluation Of Human-ai Teams For Learned And Rule-based Agents In Hanabi (2021)0.00
• Reinforcement Learning On Human Decision Models For Uniquely Collaborative AI Teammates (2021)0.00
• Enhancing Human Experience In Human-agent Collaboration: A Human-centered Modeling Approach Based On Positive Human Gain (2024)0.00
• Human-ai Coordination Via Human-regularized Search And Learning (2022)0.00
• Incorporating Human Flexibility Through Reward Preferences In Human-ai Teaming (2023)0.00
• Winning Isn't Everything: Enhancing Game Development With Intelligent Agents (2019)11.29
• Human-ai Learning Performance In Multi-armed Bandits (2018)7.50
• Adaptive Agent Architecture For Real-time Human-agent Teaming (2021)0.00