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16th International Conference on Ambient systems, Networks and Technologies Networks, ANT 2025 / 8th International Conference on Emerging Data and Industry 4.0, EDI40 2025

作者： Kliem, John Dasgupta, Prithviraj Distributed Intelligent Systems Section Naval Research Laboratory WashingtonDC20375 United States

We investigate the effect of reward shaping in improving the performance of reinforcement learning in the context of a real-time strategy game called capture-the-flag. The game is characterized by sparse rewards that are associated with infrequently occurring events such as grabbing or capturing the fag, or tagging the opponent player. We show that appropriately designed reward shaping functions applied to different game events can significantly improve the player's performance and training times of the player's learning algorithm. We have validated our reward shaping functions within a simulated environment for playing a marine capture-the-flag game between two players. Our experimental results demonstrate that reward shaping can be used as an effective means to understand the importance of different sub-tasks during game-play towards winning the game, to encode a secondary objective functions such as energy efficiency into a player's game-playing behavior, and, to improve learning generalizable policies that can perform well against different skill levels of the opponent. © 2025 Elsevier B.V.. All rights reserved.

关键词： Reinforcement learning

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Reward Shaping for Improved Learning in Attacker-Defender Game

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Procedia Computer Science 2025年 257卷 158-165页

作者： John Kliem Prithviraj Dasgupta Distributed Intelligent Systems Section Naval Research Laboratory Washington DC 20375 USA

We investigate the effect of reward shaping in improving the performance of reinforcement learning in the context of a real-time strategy game called capture-the-flag. The game is characterized by sparse rewards that are associated with infrequently occurring events such as grabbing or capturing the fag, or tagging the opponent player. We show that appropriately designed reward shaping functions applied to different game events can significantly improve the player’s performance and training times of the player’s learning algorithm. We have validated our reward shaping functions within a simulated environment for playing a marine capture-the-flag game between two players. Our experimental results demonstrate that reward shaping can be used as an effective means to understand the importance of different sub-tasks during game-play towards winning the game, to encode a secondary objective functions such as energy efficiency into a player’s game-playing behavior, and, to improve learning generalizable policies that can perform well against different skill levels of the opponent.

关键词： competitive multi-agent based systems reinforcement learning reward shaping adversarial game

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

Synthetically Generating Human-like Data for Sequential Deci...

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Synthetic Data for Artificial Intelligence and Machine Learning: Tools, Techniques, and Applications 2023

作者： Brandt, Bryan Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States Computer Science Department University of Wisconsin-Whitewater WI United States

ISBN: (纸本)9781510661721

We consider the problem of synthetically generating data that can closely resemble human decisions made in the context of an interactive human-AI system like a computer game. We propose a novel algorithm that can generate synthetic, human-like, decision making data while starting from a very small set of decision making data collected from humans. Our proposed algorithm integrates the concept of reward shaping with an imitation learning algorithm to generate the synthetic data. We have validated our synthetic data generation technique by using the synthetically generated data as a surrogate for human interaction data to solve three sequential decision making tasks of increasing complexity within a small computer game-like setup. Different empirical and statistical analyses of our results show that the synthetically generated data can substitute the human data and perform the game-playing tasks almost indistinguishably, with very low divergence, from a human performing the same tasks. © 2023 SPIE.

关键词： Decision making

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Divide and Repair: Using Options to Improve Performance of Imitation Learning Against Adversarial Demonstrations

arXiv

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arXiv 2023年

作者： Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division Naval Research Laboratory WashingtonDC United States

We consider the problem of learning to perform a task from demonstrations given by teachers or experts, when some of the experts' demonstrations might be adversarial and demonstrate an incorrect way to perform the task. We propose a novel technique that can identify parts of demonstrated trajectories that have not been significantly modified by the adversary and utilize them for learning, using temporally extended policies or options. We first define a trajectory divergence measure based on the spatial and temporal features of demonstrated trajectories to detect and discard parts of the trajectories that have been significantly modified by an adversarial expert, and could degrade the learner's performance, if used for learning, We then use an options-based algorithm that partitions trajectories and learns only from the parts of trajectories that have been determined as admissible. We provide theoretical results of our technique to show that repairing partial trajectories improves the sample efficiency of the demonstrations without degrading the learner's performance. We then evaluate the proposed algorithm for learning to play an Atari-like computer-based game called LunarLander in the presence of different types and degrees of adversarial attacks of demonstrated trajectories. Our experimental results show that our technique can identify adversarially modified parts of the demonstrated trajectories and successfully prevent the learning performance from degrading due to adversarial demonstrations. © 2023, CC BY.

关键词： Demonstrations

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Reward Shaping for Improved Learning in Real-time Strategy Game Play

arXiv

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arXiv 2023年

作者： Kliem, John Dasgupta, Prithviraj Distributed Intelligent Systems Section Information Technology Division U. S. Naval Research Laboratory WashingtonDC United States

We investigate the effect of reward shaping in improving the performance of reinforcement learning in the context of the real-time strategy, capture-the-flag game. The game is characterized by sparse rewards that are associated with infrequently occurring events such as grabbing or capturing the flag, or tagging the opposing player. We show that appropriately designed reward shaping functions applied to different game events can significantly improve the player's performance and training times of the player's learning algorithm. We have validated our reward shaping functions within a simulated environment for playing a marine capture-the-flag game between two players. Our experimental results demonstrate that reward shaping can be used as an effective means to understand the importance of different sub-tasks during game-play towards winning the game, to encode a secondary objective functions such as energy efficiency into a player's game-playing behavior, and, to improve learning generalizable policies that can perform well against different skill levels of the opponent. © 2023, CC BY.

关键词： Reinforcement learning

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Extended strategic alignment model for information systems governance

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International Journal of Applied Decision Sciences 2022年第6期15卷 681-703页

作者： Benmoussa, Khaoula Laaziri, Majida Bouayad, Abdelrhani Bennaser, Mohammed Mouchtachi, Ahmed El Yamami, Abir Structural Engineering Intelligent Systems and Electrical Energy Laboratory National Graduate School of Arts and Crafts Hassan II University Casablanca Morocco Team of Economic Research and Management of Territories Faculty of Economic and Social Legal Sciences Moulay Ismail University Meknes Morocco Training Employment and Family Research Team Higher School of Technology Moulay Ismail University Meknes Morocco Laboratory Signals Distributed Systems and Artificial Intelligence Higher Normal School of Technical Education Hassan II University Casablanca Morocco

The information systems (IS) of universities are at the heart of changes of various origins: the evolution of the processes they support and the evolution of information technologies (IT). Studies have shown great interest in strategic alignment to the private sector rather than to the public sector especially in universities. Moreover, through a review of the literature, it became apparent that there is no formal model of strategic alignment in universities. Therefore, this paper aims to take an exploratory approach to: 1) understand strategic alignment;2) evaluate its implementation within universities;3) adapt its mechanisms by proposing a new model of strategic alignment in universities based on the most widely used model (SAM). The result shows that the implementation of this proposed model can help universities achieve these goals and improve their competitiveness and effectiveness. Copyright © Inderscience Enterprises Ltd.

关键词： Information systems

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation

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Procedia computer science 2023年 220卷 339-347页

作者： Leila Ismail Muhammad Danish Waseem Clouds and Distributed Computing and Systems (CLOUDS) Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia. Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University UAE. National Water and Energy Center United Arab Emirates University UAE.

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately.

关键词： Computer Vision Deep Learning Image Processing Machine Learning Pain Detection Patient-Centric Smart healthcare eHealth

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Synthetically Generating Human-like Data for Sequential Decision Making Tasks via Reward-Shaped Imitation Learning

arXiv

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arXiv 2023年

关键词： Computer games

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

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Procedia Computer Science 2023年 220卷 331-338页

作者： Alain Hennebelle Huned Materwala Leila Ismail Independent Researcher Melbourne Australia Intelligent Distributed Computing and Systems (INDUCE) Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR.

关键词： Artificial Intelligence (AI) Cloud Computing Diagnosis Digital Health Edge Computing eHealth Internet of Things (IoT) Logisitic Regression Machine Learning Prognosis Random Forest Risk Factors Smart Connected Healthcare Type 2 Diabetes Mellitus

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Learning to Sail Dynamic Networks: The MARLIN Reinforcement Learning Framework for Congestion Control in Tactical Environments

Learning to Sail Dynamic Networks: The MARLIN Reinforcement ...

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MILCOM, Military Communications Conference

作者： Raffaele Galliera Mattia Zaccarini Alessandro Morelli Roberto Fronteddu Filippo Poltronieri Niranjan Suri Mauro Tortonesi Florida Institute for Human & Machine Cognition (IHMC) Department of Intelligent Systems & Robotics The University of West Florida (UWF) Pensacola FL USA Distributed Systems Research Group University of Ferrara Ferrara Italy US Army DEVCOM Army Research Laboratory (ARL) Adelphi MD USA

Conventional Congestion Control (CC) algorithms, such as TCP Cubic, struggle in tactical environments as they misinterpret packet loss and fluctuating network performance as congestion symptoms. Recent efforts, including our own MARLIN, have explored the use of Reinforcement Learning (RL) for CC, but they often fall short of generalization, particularly in competitive, unstable, and unforeseen scenarios. To address these challenges, this paper proposes an RL framework that leverages an accurate and parallelizable emulation environment to reenact the conditions of a tactical network. We also introduce refined RL formulation and performance evaluation methods tailored for agents operating in such intricate scenarios. We evaluate our RL learning framework by training a MARLIN agent in conditions replicating a bottleneck link transition between a Satellite Communication (SATCOM) and an UHF Wide Band (UHF) radio link. Finally, we compared its performance in file transfer tasks against Transmission Control Protocol (TCP) Cubic and the default strategy implemented in the Mockets tactical communication middleware. The results demonstrate that the MARLIN RL agent outperforms both TCP and Mockets under different perspectives and highlight the effectiveness of specialized RL solutions in optimizing CC for tactical network environments.

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