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Multi-objective reinforcement learning framework for beneficent artificial intelligence

Neural Computing and Applications 2025年 1-19页

作者： Nickelson, Anna Perkins, Russell London, Alex John Robinette, Paul Tumer, Kagan Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis USA Electrical and Computer Engineering University of Massachusetts Lowell Lowell USA Department of Philosophy Carnegie Mellon University Pittsburgh USA

As artificial intelligence (AI) systems are more widely deployed and utilized, they have greater potential to inflict harm or provide benefits to individuals. Designers must consider these concepts when building AI systems, but it is difficult to capture these notions in a mathematical framework. An ethical framework put forth by London and Heidari (Minds Mach, 2024. https://***/10.1007/s11023-024-09696-8 ) provides structure and formal definitions of benefits and harms in relation to an individual’s life plans. This leaves an open question of how these concepts impact the decision-making of an AI system. We leverage their work to provide a direct translation of these theoretical ethical concepts to a standard multi-objective reinforcement learning (MORL) decision model. Using this model, we show that multi-objective rewards are necessary to capture benefits and harms in accordance with their definitions. We demonstrate how to capture these concepts in a MORL system and provide scenarios to highlight the utility of this work.

关键词： Reinforcement learning

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Automatic speaker identification system based on MLP network and deep learning in the presence of severe interference

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Multimedia Tools and Applications 2025年 1-42页

作者： Shafik, Amira Sedik, Ahmed El-Shafai, Walid Khalaf, Ashraf A. M. El-Rabaie, El-Sayed M. El-Samie, Fathi E. Abd Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Electrical Engineering Faculty of Engineering Minia University Minia61519 Egypt Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafr Elsheikh University Kafr El‑Shaikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Automatic Speaker Identification (ASI) is so crucial for security. Current ASI systems perform well in quiet and clean surroundings. However, in noisy situations, the robustness of an ASI system against additive noise and interference is a crucial factor. An investigation of the impact of interference on ASI system performance is presented in this paper, which introduces algorithms for achieving high ASI system performance. The objective is to resist the interference of various forms. This paper presents two models for the ASI task in the presence of interference. The first one depends on Normalized Pitch Frequency (NPF) and Mel-Frequency Cepstral Coefficients (MFCCs) as extracted features and Multi-Layer Perceptron (MLP) as a classifier. In this model, we investigate the utilization of a Discrete Transform (DT), such as Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT) and Discrete Sine Transform (DST), to increase the robustness of extracted features against different types of degradation through exploiting the sub-band decomposition characteristics of DWT and the energy compaction property of DCT and DST. This is achieved by extracting features directly from contaminated speech signals in addition to features extracted from discrete transformed signals to create hybrid feature vectors. The enhancement techniques, such as Spectral Subtraction (SS), Winer Filter, and adaptive Wiener filter, are used in a preprocessing stage to eliminate the effect of the interference on the ASI system. In the second model, we investigate the utilization of Deep Learning (DL) based on a Convolutional Neural Network (CNN) with speech signal spectrograms and their Radon transforms to increase the robustness of the ASI system against interference effects. One of this paper goals is to introduce a comparison between the two models and build a more robust ASI system against severe interference. The experimental results indicate that the two proposed models lead to satisfa

关键词： Additive noise

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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IEEE Southeastcon

作者： Bhaavin Kishore Jogeshwar Joshua Bohannon Hakki Erhan Sevil Department of Intelligent Systems and Robotics University of West Florida (UWF) Pensacola FL USA Department of Electrical and Computer Engineering University of West Florida (UWF) Pensacola FL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies.

关键词： Navigation Toy manufacturing industry Software algorithms Prototypes Object detection Grasping Land vehicles Real-time systems Planning Vehicle dynamics

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Early Prediction of Cryptocurrency Price Decline: A Deep Learning Approach 26

Early Prediction of Cryptocurrency Price Decline: A Deep Lea...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Akter, Mst Shapna Shahriar, Hossain Rahman, Md Abdur Rahman, Muhammad Cuzzocrea, Alfredo University of West Florida Department of Intelligent Systems and Robotics United States University of West Florida Center for Cybersecurity United States Jahangirnagar University Department of Mathematics Dhaka Savar Bangladesh Clayton State University Department of Computer Science United States University of Calabria IDEA Lab Rende Italy

ISBN: (纸本)9798350359015

In recent times, the cryptocurrency market has emerged as one of the fastest-growing financial markets worldwide. It is, however, known for its high volatility and illiquidity compared to traditional markets such as equities, foreign exchange, and commodities. This inherent risk creates uncertainty among investors. The aim of this research is to forecast the level of risk in the cryptocurrency market. To assist cryptocurrency investors in navigating these challenges, we propose an approach that involves calculating the risk factor based on existing parameters. We employed various machine learning algorithms, including CNN, LSTM, BiLSTM, and GRU, to predict the risk factor in twenty elements of the cryptocurrency market. Through extensive experimentation, we developed a new model that outperformed existing models, achieving the highest Root Mean Square Error (RMSE) value of 1.3229 and the lowest RMSE value of 0.0089. Furthermore, we tested the generalization ability of our proposed model on a new dataset, different from the one used for training. Even with this new dataset, our model displayed robust performance. In contrast, the other existing models achieved higher RMSE values, with the highest being 14.5092 and the lowest 0.02769. By adopting our approach, investors can trade more confidently in complex and challenging financial assets such as Bitcoin, Ethereum, and Dogecoin. Our proposed model demonstrates superior performance and generalization capabilities, providing valuable insights for participants in the cryptocurrency market. © 2023 IEEE.

关键词： Cryptocurrency deep learning ensemble learning time-series forecasting volatility

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AGVs vs AMRs: A Comparative Study of Fleet Performance and Flexibility

AGVs vs AMRs: A Comparative Study of Fleet Performance and F...

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Iberian robotics Conference (ROBOT)

作者： Rita Tomé Silva Marina Brilhante Héber Sobreira Diogo Matos Pedro Costa Department of Electrical and Computer Engineering Faculty of Engineering of the University of Porto Porto Portugal Centre of Robotics in Industry and Intelligent Systems INESC TEC Porto Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

Autonomous Mobile Robots (AMRs) and Automated Guided Vehicles (AGVs) have emerged as key innovations in the industry world, with AMRs offering flexibility a nd adaptability for dynamic environments, while AGVs provide high accuracy for repetitive tasks; thus, this research proposes a study of fleets of both AGVs and AMRs to enhance productivity and efficiency in industrial settings. Several tests were performed where the duration of a mission, the success and collision rate, and the average number of disputes per mission were analyzed in order to obtain results. In conclusion, while AGVs tend to be more reliable and consistent in task completion, AMRs offer greater flexibility a nd speed.

关键词： Productivity Technological innovation Remotely guided vehicles Navigation Scalability Path planning Mobile robots Reliability Vehicle dynamics Autonomous robots

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. Beijing Institute of Technology School of Information and Electronics Beijing100081 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen518055 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2008 Australia Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2002-2012 IEEE.

关键词： Graph neural networks

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Start State Selection for Control Policy Learning from Optimal Trajectories

Start State Selection for Control Policy Learning from Optim...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christoph Zelch Jan Peters Oskar von Stryk Department of Computer Science TU Darmstadt Simulation Systems Optimization and Robotics Group Darmstadt Germany Department of Computer Science Intelligent Autonomous Systems Group TU Darmstadt Darmstadt Germany

Combination of optimal control methods and machine learning approaches allows to profit from complementary benefits of each field in control of robotic systems. Data from optimal trajectories provides valuable information that can be used to learn a near-optimal state-dependent feedback control policy. To obtain high-quality learning data, careful selection of optimal trajectories, determined by a set of start states, is essential to achieve a good learning performance. In this paper, we extend previous work with new comple-menting strategies to generate start points. These methods complement the existing approach, as they introduce new criteria to identify relevant regions in joint state space that need coverage by new trajectories. It is demonstrated that the extensions significantly improve the overall performance of the previous method in simulation on full nonlinear dynamics model of the industrial Manutec r3 robot arm. Further, it is demonstrated that it suffices to learn a policy that reaches the proximity of the goal state, from where a PI controller can be used for stable control reaching the final system state.

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Facial Expression Recognition using Recent Convolutional Neural Network Models

Facial Expression Recognition using Recent Convolutional Neu...

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International Conference on computer and Information Technology (ICCIT)

作者： Fulya Yenilmez Canan Yıldız Mukden Uğur Department of Computer Engineering Turkish-German University Istanbul Turkey Department of Robotics and Intelligent Systems Turkish-German University Istanbul Turkey

In recent years, deep learning architectures have demonstrated remarkable achievements on diverse computer vision tasks, including the recognition of facial expressions. The development of precise and robust facial expression recognition (FER) models has the potential to improve a wide range of applications in various fields, such as human-computer interaction, emotion analysis, marketing, robotics, psychology, and health *** this domain, several Convolutional Neural Network (CNN) architectures have been employed to yield promising results. In this paper, we undertake a comparative analysis with the latest iterations of five widely adopted CNN models for the FER task: VGG19, InceptionV3, ResNet152V2, MobileNetV2, and EfficientNetV2B1. We also explore the effect of transfer learning, specifically the difference between pretraining on VGGFace versus ImageNet. Our primary goal is to comprehensively assess these architectures under identical conditions and provide clarity on their performance in comparison to each other and to previous works using various training strategies. Our assessments allow the conclusion that increasing the size and depth of these common "backbone" models leads to only minor improvements. Augmenting these models with additional architectural elements such as attention mechanisms seems much more promising in comparison. Our results further show that pretraining on a domain-specific dataset can lead to significant improvements, demonstrating the importance of a large FER related dataset.

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Hyperbolic Delaunay Geometric Alignment

arXiv

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arXiv 2024年

作者： Medbouhi, Aniss Aiman Marchetti, Giovanni Luca Polianskii, Vladislav Kravberg, Alexander Poklukar, Petra Varava, Anastasia Kragic, Danica Division of Robotics Perception and Learning Department of Intelligent Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm100 44 Sweden

Hyperbolic machine learning is an emerging field aimed at representing data with a hierarchical structure. However, there is a lack of tools for evaluation and analysis of the resulting hyperbolic data representations. To this end, we propose Hyperbolic Delaunay Geometric Alignment (HyperDGA) – a similarity score for comparing datasets in a hyperbolic space. The core idea is counting the edges of the hyperbolic Delaunay graph connecting datapoints across the given sets. We provide an empirical investigation on synthetic and real-life biological data and demonstrate that HyperDGA outperforms the hyperbolic version of classical distances between sets. Furthermore, we showcase the potential of HyperDGA for evaluating latent representations inferred by a Hyperbolic Variational Auto-Encoder. © 2024, CC BY.

关键词： Geometry

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Gait Analysis and Control of Rimless Wheel Rovers for Planetary Exploration

Gait Analysis and Control of Rimless Wheel Rovers for Planet...

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Space robotics (iSpaRo), International Conference on

作者： Dennis Schlee Shubham Vyas Rauĺ Domínguez Frank Kirchner DFKI GmbH - Robotics Innovation Center Bremen Germany Intelligent Autonomous Systems Group Technical University of Darmstadt Darmstadt Germany Department of Mathematics and Computer Science AG Robotics University of Bremen Bremen Germany

ISBN: (数字)9798350367232

ISBN: (纸本)9798350367249

Rimless wheeled robots are a hybrid variant of traditionally wheeled and legged robots. These hybrid systems combine the benefits of both approaches, such as power efficiency and good terrain traversability. However, the simplicity of the design of the rimless wheel has the disadvantage of limiting the flexibility of foot placement and the impacts caused on the body when navigating rigid surfaces. In this work, we focus on analyzing the effects of different gaits on the robot’s center body. For that, a novel gait classification approach for a rimless wheeled system is introduced and the effects of selected gaits are evaluated in a simulation using a simplified robot model. The analysis of simulation results shows that the chosen gait has a strong effect on the vertical motion of the robot’s center body.

关键词： Legged locomotion Limiting Navigation Simulation Wheels Aerospace electronics Hybrid power systems

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