13 citations · 28 across the 8 of their papers we have counts for
11 papers
A Scalable Reinforcement Learning Approach for Attack Allocation in Swarm to Swarm Engagement Problems
Umut Demir, Nazim Kemal Ure
In this work we propose a reinforcement learning (RL) framework that controls the density of a large-scale swarm for engaging with adversarial swarm attacks. Although there is a si…
Quality Characteristics of a Software Platform for Human-AI Teaming in Smart Manufacturing
Philipp Haindl, Thomas Hoch, Javier Dominguez +3
As AI-enabled software systems become more prevalent in smart manufacturing, their role shifts from a reactive to a proactive one that provides context-specific support to machine…
Nonlinear Model Based Guidance with Deep Learning Based Target Trajectory Prediction Against Aerial Agile Attack Patterns
A. Sadik Satir, Umut Demir, Gulay Goktas Sever +1
In this work, we propose a novel missile guidance algorithm that combines deep learning based trajectory prediction with nonlinear model predictive control. Although missile guidan…
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions
Anil Ozturk, Mustafa Burak Gunel, Resul Dagdanov +4
Applications of reinforcement learning (RL) are popular in autonomous driving tasks. That being said, tuning the performance of an RL agent and guaranteeing the generalization perf…
Learning How to Trade-Off Safety with Agility Using Deep Covariance Estimation for Perception Driven UAV Motion Planning
Onur Akgun, Kamil Canberk Atik, Mustafa Erdem +3
We investigate how to utilize predictive models for selecting appropriate motion planning strategies based on perception uncertainty estimation for agile unmanned aerial vehicle (U…
A Probabilistic Guidance Approach to Swarm-to-Swarm Engagement Problem
Samet Uzun, Nazim Kemal Ure
This paper introduces a probabilistic guidance approach for the swarm-to-swarm engagement problem. The idea is based on driving the controlled swarm towards an adversary swarm, whe…