Adversarial Training: Research Area — Prolific Citations Library

Discover 1 peer-reviewed study in Adversarial Training (2025). Explore research findings powered by Prolific's diverse participant panel.

This page lists 1 peer-reviewed paper in the research area of Adversarial Training in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (1 of 1)

Improving Human-AI Coordination through Adversarial Training and Generative Models

Authors: Paresh Chaudhary, Yancheng Liang, Daphne Chen, Simon S. Du, Natasha Jaques

Year: 2025

Published in: ArXiv

Institution: University of Washington

Research Area: Human-AI Coordination, Zero-Shot Coordination, Adversarial Training, Generative Models

Discipline: Artificial Intelligence, Human-Computer Interaction (HCI)

The paper introduces GOAT, a novel framework combining pretrained generative models and adversarial training to improve human-AI coordination, achieving state-of-the-art performance on the Overcooked benchmark with real human partners.

Methods: The study utilized a frozen pretrained generative model to simulate cooperative agent policies and applied adversarial training to dynamically generate challenging human-AI interaction scenarios for training.

Key Findings: The effectiveness of GOAT in generalizing human-AI coordination strategies and its performance on the Overcooked benchmark.