2 citations · 5 across the 5 of their papers we have counts for
5 papers
ASA-SimaaS: Advancing Digital Transformation through Simulation Services in the Brazilian Air Force
Joao P. A. Dantas, Diego Geraldo, Andre N. Costa +2
This work explores the use of military simulations in predicting and evaluating the outcomes of potential scenarios. It highlights the evolution of military simulations and the inc…
Autonomous Agent for Beyond Visual Range Air Combat: A Deep Reinforcement Learning Approach
Joao P. A. Dantas, Marcos R. O. A. Maximo, Takashi Yoneyama
This work contributes to developing an agent based on deep reinforcement learning capable of acting in a beyond visual range (BVR) air combat simulation environment. The paper pres…
Learned Tree Search for Long-Horizon Social Robot Navigation in Shared Airspace
Ingrid Navarro, Jay Patrikar, Joao P. A. Dantas +4
The fast-growing demand for fully autonomous aerial operations in shared spaces necessitates developing trustworthy agents that can safely and seamlessly navigate in crowded, dynam…
Supervised Machine Learning for Effective Missile Launch Based on Beyond Visual Range Air Combat Simulations
Joao P. A. Dantas, Andre N. Costa, Felipe L. L. Medeiros +3
This work compares supervised machine learning methods using reliable data from constructive simulations to estimate the most effective moment for launching missiles during air com…
Enhanced Self-Organizing Map Solution for the Traveling Salesman Problem
Joao P. A. Dantas, Andre N. Costa, Marcos R. O. A. Maximo +1
Using an enhanced Self-Organizing Map method, we provided suboptimal solutions to the Traveling Salesman Problem. Besides, we employed hyperparameter tuning to identify the most cr…