HATS Lab

Human-inspired Adaptive Teaming Systems

We develop autonomous synthetic characters and intelligent agents for military training simulations using multi-agent reinforcement learning, graph neural networks, and cognitive architectures.

People

Volkan Ustun

Research Lead, Principal Investigator

Yunzhe Wang

Ph.D. Student

Soham Hans

Ph.D. Student

Tim Aris

Ph.D. Student

View all team members →

News

Dec 2025

DECOY was presented at the Winter Simulation Conference

Dec 2025

We presented our work on Scenario Generation at I/ITSEC

Recent Publications

GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents

Yunzhe Wang, Runhui Xu, Kexin Zheng, Tianyi Zhang, Jayavibhav N. Kogundi, Soham Hans, Volkan Ustun

arXiv preprint (arXiv) • 2026

Code Dataset Website

GraphAllocBench: A Flexible Benchmark for Preference-Conditioned Multi-Objective Policy Learning

Zhiheng Jiang, Yunzhe Wang, Ryan Marr, Ellen Novoseller, Benjamin Files, Volkan Ustun

arXiv preprint (arXiv) • 2026

PDF arXiv

Towards AI-Assisted Generation of Military Training Scenarios

Soham Hans, Volkan Ustun, Mark Core, Benjamin Nye, James Sterrett, Matthew Green

Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC) • 2025

PDF arXiv

Implicit Behavioral Alignment of Language Agents in High-Stakes Crowd Simulations

Yunzhe Wang, Gale Lucas, Burcin Becerik-Gerber, Volkan Ustun

Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing (EMNLP) • 2025

PDF arXiv Code Website

X-Ego: Acquiring Team-Level Tactical Situational Awareness via Cross-Egocentric Contrastive Video Representation Learning

Yunzhe Wang, Soham Hans, Volkan Ustun

arXiv preprint (arXiv) • 2025

PDF arXiv Code Dataset

View all publications →