Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi

Abstract

Deep reinforcement learning has generated superhuman AI in competitive games such as Go and StarCraft. Can similar learning techniques create a superior AI teammate for human-machine collaborative games? Will humans prefer AI teammates that improve objective team performance or those that improve subjective metrics of trust? In this study, we perform a single-blind evaluation of teams of humans and AI agents in the cooperative card game Hanabi, with both rule-based and learning-based agents. In addition to the game score, used as an objective metric of the human-AI team performance, we also quantify subjective measures of the human's perceived performance, teamwork, interpretability, trust, and overall preference of AI teammate. We find that humans have a clear preference toward a rule-based AI teammate (SmartBot) over a state-of-the-art learning-based AI teammate (Other-Play) across nearly all subjective metrics, and generally view the learning-based agent negatively, despite no statistical difference in the game score. This result has implications for future AI design and reinforcement learning benchmarking, highlighting the need to incorporate subjective metrics of human-AI teaming rather than a singular focus on objective task performance.

arXiv commentAccepted for publication at NeurIPS 2021

References in corpus (12)

• Playing Atari with Deep Reinforcement Learning
• Dota 2 with Large Scale Deep Reinforcement Learning
• StarCraft II: A New Challenge for Reinforcement Learning
• Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
• The Hanabi Challenge: A New Frontier for AI Research
• Emergent Complexity via Multi-Agent Competition
• On the Utility of Learning about Humans for Human-AI Coordination
• Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning
• Simplified Action Decoder for Deep Multi-Agent Reinforcement Learning
• "Other-Play" for Zero-Shot Coordination
• Re-determinizing Information Set Monte Carlo Tree Search in Hanabi
• Human-Level Performance in No-Press Diplomacy via Equilibrium Search

Cited by in corpus (2)

• The Role of Heuristics and Biases During Complex Choices with an AI Teammate
• Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI Teammates