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

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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

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

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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

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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

A Data-Driven Discretized CS:GO Simulation Environment to Facilitate Strategic Multi-Agent Planning Research

Yunzhe Wang, Volkan Ustun, Chris McGroarty

Proceedings of the 2025 Winter Simulation Conference (WSC) • 2025

PDF arXiv Code Website

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