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Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey

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arXiv 2023年

作者： Yang, Qin Liu, Rui Computer Science and Information Systems Department Bradley University PeoriaIL61625 United States College of Aeronautics and Engineering Kent State University KentOH44242 United States

As a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field, the utility is used to evaluate the level of individual needs, preferences, and interests. Especially for decision-making and learning in multi-agent/robot systems (MAS/MRS), a suitable utility model can guide agents in choosing reasonable strategies to achieve their current needs and learning to cooperate and organize their behaviors, optimizing the system’s utility, building stable and reliable relationships, and guaranteeing each group member’s sustainable development, similar to the human society. Although these systems’ complex, large-scale, and long-term behaviors are strongly determined by the fundamental characteristics of the underlying relationships, there has been less discussion on the theoretical aspects of mechanisms and the fields of applications in Robotics and AI. This paper introduces a utility-orient needs paradigm to describe and evaluate inter and outer relationships among agents’ interactions. Then, we survey existing literature in relevant fields to support it and propose several promising research directions along with some open problems deemed necessary for further investigations. Copyright © 2023, The Authors. All rights reserved.

关键词： Robotics

Adaptive Strategy Templates Using Deep Reinforcement Learning for Multi-Issue Bilateral Negotiation

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SSRN

SSRN 2023年

作者： Bagga, Pallavi Paoletti, Nicola Stathis, Kostas Moorfields Eye Hospital NHS Foundation Trust London United Kingdom Department of Informatics King’s College London University of London United Kingdom Department of Computer Science Royal Holloway University of London United Kingdom

We introduce a novel negotiation model enabling an agent to learn a heuristic strategy from a given set of predefined tactics, forming a "strategy template." This template includes (a) choice parameters determining whether a tactic should be included in the strategy;(b) time parameters determining when should a tactic be triggered;and (c) attribute-value parameters determining values that constraint acceptance or bidding. The model accelerates learning through pre-training with teacher strategies and online training in diverse settings, yielding adaptive, non-pre-programmed, and transferable strategies. It handles user preference uncertainty, deriving a user model approximating partially specified preferences at negotiation start. A proof-of-concept prototype is developed using an actor-critic architecture based on deep reinforcement learning. Stochastic search derives the user model to handle preference uncertainty. A combination of multi-objective optimization and multi- criteria decision-making methods is used as one of the bidding tactics to generate near Pareto-optimal bids during negotiation. Extensive experimental evaluation demonstrates our model outperforms state-of-the-art in terms of both individual and social-welfare utilities. Combining learning from teacher strategies and different choice, time and attribute value parameters enables experience transfer across domains. This provides a significant advantage when confronting unfamiliar opponents in previously unseen negotiation scenarios. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

Extracting Stress-Related EEG Patterns from Pre-Sleep EEG for Forecasting Slow-Wave Sleep Deficiency

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IEEE Transactions on Neural systems and Rehabilitation Engineering 2024年 32卷 1817-1827页

作者： Su, Cheng-Hua Ko, Li-Wei Jung, Tzyy-Ping Onton, Julie Tzou, Shey-Cherng Juang, Jia-Chi Hsu, Chung-Yao Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu300 Taiwan Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu300 Taiwan Kaohsiung Medical University Drug Development and Value Creation Research Center Department of Biomedical Science and Environmental Biology Kaohsiung807 Taiwan University of California San Diego Swartz Center for Computational Neuroscience La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan University of California San Diego Institute for Neural Computation La Jolla CA92093 United States National Yang Ming Chiao Tung University Department of Biological Science and Technology Hsinchu300 Taiwan Kaohsiung Medical University Hospital Sleep Disorders Center Department of Neurology Kaohsiung807 Taiwan Kaohsiung Medical University College of Medicine Department of Neurology Kaohsiung807 Taiwan

Sleep is vital to our daily activity. Lack of proper sleep can impair functionality and overall health. While stress is known for its detrimental impact on sleep quality, the precise effect of pre-sleep stress on subsequent sleep structure remains unknown. This study introduced a novel approach to study the pre-sleep stress effect on sleep structure, specifically slow-wave sleep (SWS) deficiency. To achieve this, we selected forehead resting EEG immediately before and upon sleep onset to extract stress-related neurological markers through power spectra and entropy analysis. These markers include beta/delta correlation, alpha asymmetry, fuzzy entropy (FuzzEn) and spectral entropy (SpEn). Fifteen subjects were included in this study. Our results showed that subjects lacking SWS often exhibited signs of stress in EEG, such as an increased beta/delta correlation, higher alpha asymmetry, and increased FuzzEn in frontal EEG. Conversely, individuals with ample SWS displayed a weak beta/delta correlation and reduced FuzzEn. Finally, we employed several supervised learning models and found that the selected neurological markers can predict subsequent SWS deficiency. Our investigation demonstrated that the classifiers could effectively predict varying levels of slow-wave sleep (SWS) from pre-sleep EEG segments, achieving a mean balanced accuracy surpassing 0.75. The SMOTE-Tomek resampling method could improve the performance to 0.77. This study suggests that stress-related neurological markers derived from pre-sleep EEG can effectively predict SWS deficiency. Such information can be integrated with existing sleep-improving techniques to provide a personalized sleep forecasting and improvement solution. © 2001-2011 IEEE.

关键词： Entropy

Enhancing Physical Layer Communication Security through Generative AI with Mixture of Experts

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arXiv 2024年

作者： Zhao, Changyuan Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui In Kim, Dong Shen, Xuemin Letaief, Khaled B. CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computing and Data Science Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Hong Kong University of Science and Technology Hong Kong

AI technologies have become more widely adopted in wireless communications. As an emerging type of AI technologies, the generative artificial intelligence (GAI) gains lots of attention in communication security. Due to its powerful learning ability, GAI models have demonstrated superiority over conventional AI methods. However, GAI still has several limitations, including high computational complexity and limited adaptability. Mixture of Experts (MoE), which uses multiple expert models for prediction through a gate mechanism, proposes possible solutions. Firstly, we review GAI model’s applications in physical layer communication security, discuss limitations, and explore how MoE can help GAI overcome these limitations. Furthermore, we propose an MoE-enabled GAI framework for network optimization problems for communication security. To demonstrate the framework’s effectiveness, we provide a case study in a cooperative friendly jamming scenario. The experimental results show that the MoE-enabled framework effectively assists the GAI algorithm, solves its limitations, and enhances communication security. © 2024, CC BY.

关键词： Network security

Blockchain-based Edge Computing Service with Dynamic Entry and Exit Mechanism

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IEEE Transactions on Mobile Computing 2025年

作者： He, Qiang Feng, Zheng Fang, Hui Wang, Xingwei Zhao, Liang Yu, Keping Choo, Kim-Kwang Raymond Northeastern University College of Medicine and Biological Information Engineering China Shanghai University of Finance and Economics Research Institute for Interdisciplinary Science School of Information Management and Engineering China Northeastern University College of Computer Science and Engineering Shenyang110169 China Shenyang Aerospace University College of Computer Science and Engineering Shenyang110136 China Hosei University Graduate School of Science and Engineering Tokyo184-8584 Japan University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

With the widespread application of 5G and artificial intelligence (AI) technology, the Internet of Things (IoT) has been expanding and integrated into various aspects of our daily lives. However, this also poses challenges such as the ubiquitous demand for communication and computing resources, and data privacy issues. Considering its flexible deployment, high security, and ease of scalability, blockchain-enabled edge computing IoT network (BECIN) has become a promising solution to provide secure and fast communication and computing services. However, existing research on computation offloading in edge computing largely overlooks the stochastic arrival of computational tasks and the potential variability in the number, locations, and resource provisions of edge computing service providers. Therefore, we propose a dynamic, self-adjusting BECIN framework aimed at providing long-term stable, efficient, and secure edge computing data offloading services for ground users in a specific region. This framework supports the dynamic entry and exit of edge computing service providers. Additionally, we introduce a novel dynamic Dueling DDQN approach to update the offloading and resource management policies based on changes in resource provisioning. Experimental results demonstrate the feasibility and superior performance of our framework on system cost and system latency. © 2002-2012 IEEE.

关键词： Reinforcement learning

Combining R-Tree and B-Tree to Enhance Spatial Queries Processing 23

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Combining R-Tree and B-Tree to Enhance Spatial Queries Proce...

23rd International Conference on computer Theory and Applications, ICCTA 2013

作者： Abd Elwahab, Marwa A. M. Mahar, Khaled M. Abdelkader, Hatem Khater, Hatem Awad Arab Academy for Science and Technology College of Computing and Information Technology Alexandria Egypt Monofia University Faculty of Computer and Information Systems Information Systems Department Alexandria Egypt Egyptian Naval Forces Research and Development Department Egypt

ISBN: (纸本)9781479924158

The search process in the spatial database consumes much time for the user. The size of the database determines the efficacy and speed of information retrieval. The potency of spatial query in Geographic Information systems (GIS) is of paramount importance. It depends heavily on the query processing algorithms. When the database size increases, the processes of data retrieval gets complicated. This paper employs two types of data structures;Rectangle-Tree (R-tree) and the Balanced-Tree (B-tree) which is called aRB-Tree. These two types have been formerly merged by researchers to facilitate the retrieval of data as quickly as possible while providing efficient results. The same idea has been adopted in this paper where the aRB (aggregate RB) accelerates executing complex queries and minimizes the time consumed in searching, editing, deleting and updating any record in the spatial database. In this paper a web application was developed to test the efficiency of using both data structures. This web application utilises a medical institution database that contains locations such as addresses of physicians, clinics, hospital, labs, and radiology centres. Results of aRB-Tree are more sufficient and accurate than using each index alone. © 2013 IEEE.

关键词： Geographic information systems

A User-Centered, Interactive, Human-in-the-Loop Topic Modelling System

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arXiv 2023年

作者： Fang, Zheng Alqazlan, Lama Liu, Du He, Yulan Procter, Rob Department of Computer Science University of Warwick United Kingdom Bayes Business School City University of London United Kingdom Department of Informatics King's College London United Kingdom The Alan Turing Institute United Kingdom

Human-in-the-loop topic modelling incorporates users' knowledge into the modelling process, enabling them to refine the model iteratively. Recent research has demonstrated the value of user feedback, but there are still issues to consider, such as the difficulty in tracking changes, comparing different models and the lack of evaluation based on real-world examples of use. We developed a novel, interactive human-in-the-loop topic modeling system with a user-friendly interface that enables users compare and record every step they take, and a novel topic words suggestion feature to help users provide feedback that is faithful to the ground truth. Our system also supports not only what traditional topic models can do, i.e., learning the topics from the whole corpus, but also targeted topic modelling, i.e., learning topics for specific aspects of the corpus. In this article, we provide an overview of the system and present the results of a series of user studies designed to assess the value of the system in progressively more realistic applications of topic modelling. © 2023, CC BY.

关键词： Learning systems

Guaranteeing Control Requirements via Reward Shaping in Reinforcement Learning

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arXiv 2023年

作者： De Lellis, Francesco Coraggio, Marco Russo, Giovanni Musolesi, Mirco Bernardo, Mario di The Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy The Scuola Superiore Meridionale School for Advanced Studies Naples Italy The Department of Computer and Electrical Engineering & Applied Mathematics University of Salerno DIEM Salerno Italy The Department of Computer Science University College London United Kingdom The Department of Informatics - Science and Engineering University of Bologna Bologna Italy

In addressing control problems such as regulation and tracking through reinforcement learning, it is often required to guarantee that the acquired policy meets essential performance and stability criteria such as a desired settling time and steady-state error prior to deployment. Motivated by this necessity, we present a set of results and a systematic reward shaping procedure that (i) ensures the optimal policy generates trajectories that align with specified control requirements and (ii) allows to assess whether any given policy satisfies them. We validate our approach through comprehensive numerical experiments conducted in two representative environments from OpenAI Gym: the Inverted Pendulum swing-up problem and the Lunar Lander. Utilizing both tabular and deep reinforcement learning methods, our experiments consistently affirm the efficacy of our proposed framework, highlighting its effectiveness in ensuring policy adherence to the prescribed control requirements. © 2023, CC BY-NC-SA.

关键词： Reinforcement learning

Deep convolutional neural networks accurately predict breast cancer using mammograms

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Waves in Random and Complex Media 2023年

作者： Hussain, Lal Ansari, Sara Shabir, Mamoona Qureshi, Shahzad Ahmad Aldweesh, Amjad Omar, Abdulfattah Iqbal, Zahoor Bukhari, Syed Ahmed Chan Department of Computer Science & IT The University of Azad Jammu and Kashmir Neelum Campus Muzaffarabad Pakistan Department of Computer Science & IT The University of Azad Jammu and Kashmir King Abdullah Campus Muzaffarabad Pakistan The Children’s Hospital University of Child Sciences Lahore Pakistan Services Institute of Medical Sciences Lahore Pakistan Islamabad Pakistan College of Computer Science and Information Technology Shaqra University Shaqra Saudi Arabia Department of English College of Science & Humanities Prince Sattam Bin Abdulaziz University Al Kharj Saudi Arabia Department of Mathematics Quaid-i-Azam University Islamabad Pakistan Healthcare Informatics St. John’s University Queens NY United States

Breast cancer in women is the most frequently diagnosed and major leading cause of cancer deaths. Due to the complex nature of microcalcification and masses, radiologists fail to diagnose breast cancer properly. In this research paper, we have employed a novel Deep Convolutional Neural Network (DCNN) model using a transfer learning strategy and compared the results with Machine Learning (ML) techniques such as Support vector machine (SVM) kernels and Decision Trees based on different features extracting strategies to distinguish cancer mammograms from normal subjects. In this study, we first extracted the hand-crafted features such as as texture, morphological, entropy-based, scale-invariant feature transform (SIFT), and elliptic Fourier descriptors (EFDs) and fed into machine learning algorithm for classification. We then utilized the deep learning algorithms with transfer learning approach. The deep learning models yielded the highest detection performance with default and optimized parameters i.e. GoogleNet yielded accuracy (99.26%), AUC (0.9998) with default parameters and AlexNet yielded accuracy (99.26%), AUC (0.9996) with optimized parameters. The results reveal that proposed approach is more robust for early detection of breast mammograms which can be best utilized for improved diagnosis and prognosis. © 2023 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Support vector machines