13 citations · 60 across the 24 of their papers we have counts for
24 papers
Resource Allocation under Stochastic Demands using Shrinking Horizon Optimization
Alexandros E. Tzikas, Nazim Kemal Ure, Mansur Arief +2
We consider the problem of optimally allocating a limited number of resources across time to maximize revenue under stochastic demands. This formulation is relevant in various area…
Iterative Active-Inactive Obstacle Classification for Time-Optimal Collision Avoidance
Mehmetcan Kaymaz, Nazim Kemal Ure
Time-optimal obstacle avoidance is a prevalent problem encountered in various fields, including robotics and autonomous vehicles, where the task involves determining a path for a m…
An Integrated Imitation and Reinforcement Learning Methodology for Robust Agile Aircraft Control with Limited Pilot Demonstration Data
Gulay Goktas Sever, Umut Demir, Abdullah Sadik Satir +2
In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and ai…
Beyond Traditional DoE: Deep Reinforcement Learning for Optimizing Experiments in Model Identification of Battery Dynamics
Gokhan Budan, Francesca Damiani, Can Kurtulus +1
Model identification of battery dynamics is a central problem in energy research; many energy management systems and design processes rely on accurate battery models for efficiency…
Reinforcement Learning Based Self-play and State Stacking Techniques for Noisy Air Combat Environment
Ahmet Semih Tasbas, Safa Onur Sahin, Nazim Kemal Ure
Reinforcement learning (RL) has recently proven itself as a powerful instrument for solving complex problems and even surpassed human performance in several challenging application…
IQ-Flow: Mechanism Design for Inducing Cooperative Behavior to Self-Interested Agents in Sequential Social Dilemmas
Bengisu Guresti, Abdullah Vanlioglu, Nazim Kemal Ure
Achieving and maintaining cooperation between agents to accomplish a common objective is one of the central goals of Multi-Agent Reinforcement Learning (MARL). Nevertheless in many…
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous Driving
Resul Dagdanov, Feyza Eksen, Halil Durmus +2
Safely navigating through an urban environment without violating any traffic rules is a crucial performance target for reliable autonomous driving. In this paper, we present a Rein…
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…
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms
Resul Dagdanov, Halil Durmus, Nazim Kemal Ure
In this work, we propose a self-improving artificial intelligence system to enhance the safety performance of reinforcement learning (RL)-based autonomous driving (AD) agents using…
Obstacle Identification and Ellipsoidal Decomposition for Fast Motion Planning in Unknown Dynamic Environments
Mehmetcan Kaymaz, Nazim Kemal Ure
Collision avoidance in the presence of dynamic obstacles in unknown environments is one of the most critical challenges for unmanned systems. In this paper, we present a method tha…
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning
Doğay Kamar, Nazım Kemal Üre, Gözde Ünal
In this study, we address the problem of efficient exploration in reinforcement learning. Most common exploration approaches depend on random action selection, however these approa…
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…
Evaluating Generalization and Transfer Capacity of Multi-Agent Reinforcement Learning Across Variable Number of Agents
Bengisu Guresti, Nazim Kemal Ure
Multi-agent Reinforcement Learning (MARL) problems often require cooperation among agents in order to solve a task. Centralization and decentralization are two approaches used for…
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…
PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation
Reyhan Kevser Keser, Aydin Ayanzadeh, Omid Abdollahi Aghdam +3
One of the most efficient methods for model compression is hint distillation, where the student model is injected with information (hints) from several different layers of the teac…
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…
Decentralized State-Dependent Markov Chain Synthesis with an Application to Swarm Guidance
Samet Uzun, Nazim Kemal Ure, Behcet Acikmese
This paper introduces a decentralized state-dependent Markov chain synthesis (DSMC) algorithm for finite-state Markov chains. We present a state-dependent consensus protocol that a…
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…
A New Approach for Tactical Decision Making in Lane Changing: Sample Efficient Deep Q Learning with a Safety Feedback Reward
M. Ugur Yavas, N. Kemal Ure, Tufan Kumbasar
Automated lane change is one of the most challenging task to be solved of highly automated vehicles due to its safety-critical, uncertain and multi-agent nature. This paper present…
Sample Efficient Interactive End-to-End Deep Learning for Self-Driving Cars with Selective Multi-Class Safe Dataset Aggregation
Yunus Bicer, Ali Alizadeh, Nazim Kemal Ure +2
The objective of this paper is to develop a sample efficient end-to-end deep learning method for self-driving cars, where we attempt to increase the value of the information extrac…
Development of A Stochastic Traffic Environment with Generative Time-Series Models for Improving Generalization Capabilities of Autonomous Driving Agents
Anil Ozturk, Mustafa Burak Gunel, Melih Dal +2
Automated lane changing is a critical feature for advanced autonomous driving systems. In recent years, reinforcement learning (RL) algorithms trained on traffic simulators yielded…
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment
Ali Alizadeh, Majid Moghadam, Yunus Bicer +3
Autonomous lane changing is a critical feature for advanced autonomous driving systems, that involves several challenges such as uncertainty in other driver's behaviors and the tra…
Decentralized Control of Partially Observable Markov Decision Processes using Belief Space Macro-actions
Shayegan Omidshafiei, Ali-akbar Agha-mohammadi, Christopher Amato +1
The focus of this paper is on solving multi-robot planning problems in continuous spaces with partial observability. Decentralized partially observable Markov decision processes (D…