collaborators

5 papers

eess.SY2026

A Computational Framework for Cross-Domain Mission Design and Onboard Cognitive Decision Support

J. de Curtò, Adrianne Schneider, Ricardo Yanez +18

The design of distributed autonomous systems for operation beyond reliable ground contact presents a fundamental tension: as round-trip communication latency grows, the set of deci…

cs.RO2025

Coordinated Strategies in Realistic Air Combat by Hierarchical Multi-Agent Reinforcement Learning

Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1

Achieving mission objectives in a realistic simulation of aerial combat is highly challenging due to imperfect situational awareness and nonlinear flight dynamics. In this work, we…

cs.AI2025

Towards Human Engagement with Realistic AI Combat Pilots

Ardian Selmonaj, Giacomo Del Rio, Adrian Schneider +1

We present a system that enables real-time interaction between human users and agents trained to control fighter jets in simulated 3D air combat scenarios. The agents are trained i…

cs.MA2025

Explaining Strategic Decisions in Multi-Agent Reinforcement Learning for Aerial Combat Tactics

Ardian Selmonaj, Alessandro Antonucci, Adrian Schneider +2

Artificial intelligence (AI) is reshaping strategic planning, with Multi-Agent Reinforcement Learning (MARL) enabling coordination among autonomous agents in complex scenarios. How…

cs.AI2025

Enhancing Aerial Combat Tactics through Hierarchical Multi-Agent Reinforcement Learning

Ardian Selmonaj, Oleg Szehr, Giacomo Del Rio +3

This work presents a Hierarchical Multi-Agent Reinforcement Learning framework for analyzing simulated air combat scenarios involving heterogeneous agents. The objective is to iden…