Trait-Consistent Population Modeling for Human–AI Zero-Shot Cooperation

Human–AI and multi-agent zero-shot cooperation (ZSC) requires agents that generalize to unseen human and novel agent behaviors. Population-based training is a central paradigm for ZSC, with the key challenge of designing training populations that capture the structured diversity of human behavior. To cope with this challenge, we introduce **Trait-Embedded Agent Modeling (TEAM)**, a framework for inducing human-aligned behaviors in the training population. Drawing on studies of human gameplay, particularly the widely adopted Bartle's taxonomy, our proposed population exhibits traits inspired by general behavioral patterns observed in social games, without requiring task-specific reward modifications. We instantiate this idea through three lightweight modifications to the learning objectives, yielding a population of socializers, achievers, and explorers. To support our approach of pattern-focused diversity, we formalize ZSC as training on a set of Markov decision processes (MDPs), with a newly sampled MDP at test time. This connects ZSC to existing generalization theory and quantitatively motivates focused population coverage. Our evaluation shows that TEAM outperforms strong baselines on AI-AI and real-human tests on challenging ZSC benchmarks, including Overcooked and StarCraft. We further provide an empirical space-coverage study of the trained population, showing TEAM induces semantically meaningful and interpretable state-action space coverage.