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检索条件"任意字段=4th International Conference on Optimization Learning Algorithms and Applications"

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learning Sampling Policy to Achieve Fewer Queries for Zeroth-Order optimization 27

Learning Sampling Policy to Achieve Fewer Queries for Zeroth...

引用

27th international conference on Artificial Intelligence and Statistics (AISTATS)

作者： Zhai, Zhou Shi, Wanli Huang, Heng Chang, Yi Gu, Bin Nanjing Univ Informat Sci & Technol Nanjing Peoples R China Mohamed Bin Zayed Univ Artificial Intelligence Abu Dhabi U Arab Emirates Univ Maryland College Pk MD USA Jilin Univ Sch Artificial Intelligence Changchun Peoples R China

Zeroth-order (ZO) methods, which use the finite difference of two function evaluations (also called ZO gradient) to approximate first-order gradient, have attracted much attention recently in machine learning because of their broad applications. the accuracy of the ZO gradient highly depends on how many finite differences are averaged, which are intrinsically determined by the number of perturbations randomly drawn from a distribution. Existing ZO methods try to learn a data-driven distribution for sampling the perturbations to improve the efficiency of ZO optimization (ZOO) algorithms. In this paper, we explore a new and parallel direction, i.e., learn an optimal sampling policy instead of using a random strategy to generate perturbations based on the techniques of reinforcement learning (RL), which makes it possible to approximate the gradient with only two function evaluations. Specifically, we first formulate the problem of learning a sampling policy as a Markov decision process. then, we propose our ZO-RL algorithm, i.e., using deep deterministic policy gradient, an actor-critic RL algorithm to learn a sampling policy that can guide the generation of perturbed vectors in getting ZO gradients as accurately as possible. Importantly, the existing ZOO algorithms for learning a distribution can be plugged in to improve the exploration of ZO-RL. Experimental results with different ZO estimators show that our ZO-RL algorithm can effectively reduce the query complexity of ZOO algorithms and converge faster than existing ZOO algorithms, especially in the later stage of the optimization process.

关键词： Function evaluation

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Intelligent Approaches for Optimal Irrigation Management: A Comprehensive Review 4th

Intelligent Approaches for Optimal Irrigation Management: A ...

引用

4th international conference on Digital Technologies and applications (ICDTA)

作者： Amraouy, Okacha Benbrahim, Mohammed Kabbaj, Mohammed Nabil Sidi Mohamed Ben Abdellah Univ Engn Modeling & Anal Syst Lab Fes Morocco

ISBN: (纸本)9783031686597;9783031686603

the complexity of irrigation management necessitates the use of professional experience for effective decision-making. Expert systems (ES) are specifically engineered to mimic human expertise efficiently, allowing cognitive mechanisms to be replicated at the irrigation level. the value of combining human knowledge with cutting-edge technologies such as fog computing, cloud computing, blockchain, IoT, and machine learning has recently come to be recognized more and more. the objective is to create an integrated platform that increases irrigation systems' resilience and adaptability while also increasing their accuracy, efficiency, and capacity for adaptive decision-making in light of agricultural environments' changing conditions. With a focus on ES, fuzzy logic, and machine learning algorithms, this paper examines the research trends and applicability of intelligent approaches for optimal irrigation management scenarios. It also provides a thorough analysis of how these approaches contribute to the overall success of smart irrigation management.

关键词： Intelligent Irrigation Optimal Automated Management Machine learning Expert Systems Fuzzy Logic

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OCEAN-MBRL: Offline Conservative Exploration for Model-Based Offline Reinforcement learning 38

OCEAN-MBRL: Offline Conservative Exploration for Model-Based...

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38th AAAI conference on Artificial Intelligence (AAAI) / 36th conference on Innovative applications of Artificial Intelligence / 14th Symposium on Educational Advances in Artificial Intelligence

作者： Wu, Fan Zhang, Rui Yi, Qi Gao, Yunkai Guo, Jiaming Peng, Shaohui Lan, Siming Han, Husheng Pan, Yansong Yuan, Kaizhao Jin, Pengwei Chen, Ruizhi Chen, Yunji Li, Ling Chinese Acad Sci Inst Software Intelligent Software Res Ctr Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Chinese Acad Sci Inst Comp Technol SKL Processors Beijing Peoples R China Univ Sci & Technol China Hefei Peoples R China

ISBN: (纸本)1577358872

Model-based offline reinforcement learning (RL) algorithms have emerged as a promising paradigm for offline RL. these algorithms usually learn a dynamics model from a static dataset of transitions, use the model to generate synthetic trajectories, and perform conservative policy optimization within these trajectories. However, our observations indicate that policy optimization methods used in these model-based offline RL algorithms are not effective at exploring the learned model and induce biased exploration, which ultimately impairs the performance of the algorithm. To address this issue, we propose Offline Conservative ExplorAtioN (OCEAN), a novel rollout approach to model-based offline RL. In our method, we incorporate additional exploration techniques and introduce three conservative constraints based on uncertainty estimation to mitigate the potential impact of significant dynamic errors resulting from exploratory transitions. Our work is a plug-in method and can be combined with classical model-based RL algorithms, such as MOPO, COMBO, and RAMBO. Experiment results of our method on the D4RL MuJoCo benchmark show that OCEAN significantly improves the performance of existing algorithms.

关键词： Reinforcement learning

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Research of Geoelectrical Model Identification for DC Resistivity Method Based on Deep learning 4

Research of Geoelectrical Model Identification for DC Resist...

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4th IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2024

作者： Dong, Guanliang Li, Junjian Bai, Yu Luan, Hui State Grid Jilin Province Electric Power Research Institute Changchun China Jilin University College of Instrumentation & Electrical Engineering Changchun China

ISBN: (纸本)9798350361643

As an important branch of geophysics, the nonlinear identification method has played a unique role, while deep learning is still in the initial stage of geophysical research. To identify the geoelectric model of the direct current (DC) resistivity method, this paper has established a convolutional neural network model under the TensorFlow framework in deep learning. Ten typical kinds of abnormal body model datasets were established by finite difference numerical calculation. To select the appropriate network parameters, the network is tested with different batch sizes, dropout rates, and optimization algorithms. After training the established convolutional neural network, the accuracy reached 97.1% and the loss degree was reduced to less than 0.087. To prove the accuracy of the identification network, six sets of abnormal body model data are selected to test the trained identification network, and the results show that the identification network can effectively identify the geoelectric model. © 2024 IEEE.

关键词： Convolutional neural networks

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Parametrized Quantum Circuits for Reinforcement learning 4

Parametrized Quantum Circuits for Reinforcement Learning

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4th international Multidisciplinary Information Technology and Engineering conference, IMITEC 2024

作者： Mpofu, K.T. Mthunzi-Kufa, P. Biophotonics Council for Scientific and Industrial Research National Laser Centre Gauteng Pretoria0001 South Africa University of Cape Town Division of Biomedical Engineering Department of Human Biology Western Cape Cape Town7701 South Africa College of Graduate Studies University of South Africa Pretoria South Africa

ISBN: (纸本)9798350387988

Research on quantum computing is still in its infancy, but it has a lot of potential uses. One topic with potential is machine learning, namely in the field of reinforcement learning. this work examines the integration of parametrized quantum circuits (PQC) into reinforcement learning (RL) algorithms, assessing the potential of quantum-enhanced models to solve classical RL tasks. It closely follows the example found on the TensorFlow website. this paper reviews applications of quantum reinforcement learning (QRL). We examine PQCs in a standard RL scenario, the CartPole-v1 environment from Gym, using TensorFlow Quantum and Cirq, to evaluate the relative performance of quantum versus conventional models. In comparison to conventional deep neural network (ONN) models, PQCs show slower convergence and higher processing needs, even if they are still able to learn the task and perform competitively. After they are fully trained, the quantum models show unique difficulties during the early training stages and reach a performance stability level like classical methods. this study sheds light on the present constraints as well as possible uses of quantum computing in reinforcement learning, particularly in situations with intricate, high-dimensional settings that prove difficult for classical computers to handle effectively. As we look to the future, we suggest that investigating hybrid quantum-classical algorithms, developing quantum hardware, and using quantum RL for increasingly difficult tasks are essential first steps. the study presents findings from both a classical reinforcement learning algorithm and a quantum integrated reinforcement learning algorithm. To provide a reliable comparison between quantum reinforcement algorithms and their classical equivalents, further work remains. this work lays the groundwork for future advances in the field by investigating the viability and use of quantum algorithms in reinforcement learning, even if it is not particularly unique

关键词： Quantum computers

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2024 11th international conference on Reliability, Infocom Technologies and optimization (Trends and Future Directions), ICRITO 2024

2024 11th International Conference on Reliability, Infocom T...

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11th international conference on Reliability, Infocom Technologies and optimization, ICRITO 2024

ISBN: (纸本)9798350350357

the proceedings contain 360 papers. the topics discussed include: comparative analysis of machine learning and optimization techniques in chest X-ray Image Analysis for medical diagnosis;leveraging Mongo DB for efficient data storage in MERN;authentication for online fraud detection through hidden Markov model;deep learning approach for optimal segmentation and classification of multi-class skin cancer;a machine learning system for predicting severity under single transmission line outages;survey on clustering problems using metaheuristic algorithms;an approach to recognize efficient deep learning model for pattern recognition;designing and development of AR based application for studying magnetism;a survey on blockchain for rental lease management;automated multi-page document classification and information extraction for insurance applications using deep learning techniques;and global perspectives in facial recognition for cyber security: a bibliometeric analysis.

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Optimizing Energy Management and Load Balancing through AI-Driven Quantum Approximate optimization 4

Optimizing Energy Management and Load Balancing Through AI-D...

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4th IEEE international conference on Mobile Networks and Wireless Communications, ICMNWC 2024

作者： Manjunath Kamath, K. Manasa, M. Anitha, B.S. Mahadevi Tlajiya, Falguni Chavan, Sangramjit Department of Cyber Security Karkala India Brindavan College of Engineering Department of Artificial Intelligence and Machine Learning Bengaluru India Sarada Vilas College Department of Computer Science Mysuru India Parul institute of Engineering and Technology Parul University Department of Electrical Engineering Gujarat India Pune India

ISBN: (纸本)9798350352931

the integration of Quantum Approximate optimization Algorithm (QAOA) and Quantum Annealing (QA) offers a promising approach to addressing energy management and load balancing challenges in modern power systems. this study explores the application of these quantum computing techniques, coupled with Artificial Intelligence (AI), to optimize energy distribution across smart grids. the proposed hybrid model leverages the problem-solving capabilities of QAOA for discrete optimization and the real-time adaptability of QA for minimizing energy consumption and operational costs. AI techniques are employed to predict energy demand, balance loads, and ensure efficient resource allocation. Simulations were conducted on a smart grid scenario involving renewable energy sources, varying demand, and distributed energy generation. the results demonstrate that the hybrid model achieved a 25% improvement in load balancing efficiency and a 30% reduction in energy wastage compared to traditional optimization methods. Additionally, the model reduced peak energy demand by 20%, contributing to overall grid stability. the quantum-enhanced approach also decreased computational complexity by 40%, enabling faster decision-making in dynamic environments. these findings highlight the potential of combining quantum algorithms with AI for sustainable and efficient energy management. © 2024 IEEE.

关键词： Quantum computers

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Security Consensus of Multi-agent Systems under Cyber-attack: An Iterative learning Control Strategy 6

Security Consensus of Multi-agent Systems under Cyber-attack...

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6th international conference on Data-Driven optimization of Complex Systems (DOCS)

作者： Luo, Zijian Yang, Hongyu Jiang, Jing Civil Aviat Flight Univ China Sch Sci Chengdu 641419 Peoples R China

ISBN: (纸本)9798350377859;9798350377842

this paper focuses on the security consensus problem of multi-agent systems under cyber-attack. Two types of cyber-attack are considered in our research, this situation is more common in real applications. these two types of network attacks (deception and DoS attacks) may occur alternately, resulting in the infeasibility to obtain actual output measurements. To reach the system security, a novel iterative learning control strategy is designed to analyze the issue. then, sufficient conditions are proposed from the view of the trajectory tracking, and our achieved convergence conditions are relatively simpler in comparison to the existing literature. Furthermore, the theoretical result in this paper is an extension of existing research. Finally, a numerical simulation is presented for illustration.

关键词： Security consensus Multi-agent system ILC Convergence analysis

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HOTS: A Containers Resource Allocation Hybrid Method Using Machine learning and optimization 7th

HOTS: A Containers Resource Allocation Hybrid Method Using M...

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7th international conference on optimization and learning

作者： Leclercq, E. Rivalan, Jonathan Smile Ile De France France

ISBN: (纸本)9783031779404;9783031779411

One of the main challenges in cloud computing is resource management, the ability to schedule workloads and services over the infrastructure in the most automated way. By optimizing cloud assignment and resource usage, energy can be saved, production incident can be anticipated and services QoS improved. With the recent years emergence of light virtualisation, known as containerization, the resource allocation problem was brought back, notably to support containers elasticity, hence the dynamic allocation of ressource at runtime at a single service scale. In this paper we show that using an hybrid loop system, which combines unsupervised learning and optimization techniques, our algorithm provides and iteratively improves scheduling solutions to containers resource assignment, enabling capacity planning over dynamic resource loads. Within our benchmarks, these solutions outperform state of the art algorithms, by an average of 6.3%, while providing more expressivity and control over input parameters. We describe also the implementation of this method, through an open source Python library called HOTS, which allows hybrid optimization for time series based use cases.

关键词： Clustering Continuous optimization Containerized applications Resource Allocation

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Optimizing Feature Selection in Spam Email Detection Using Co-Evolutionary algorithms 8

Optimizing Feature Selection in Spam Email Detection Using C...

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8th SLAAI - international conference on Artificial Intelligence, SLAAI-ICAI 2024

作者： Wanigasingha, Nethni Hirusha Fernando, T.G.I. University of Sri Jayewardenepura Department of Computer Science Nugegoda Sri Lanka

ISBN: (纸本)9798331509170

the Co-Evolutionary algorithms for Feature Selections delve into feature selection in the context of data-rich environments. the study aims to identify and implement a suitable co-evolutionary feature selection method for the classification of spam emails. By exploring co-evolutionary algorithms and their application in solving feature selection challenges, the research investigates the transformation of feature selection issues into optimization problems. through the utilization of co-evolutionary algorithms, the study analyzes and compares the outcomes with other established feature selection methods in machine learning. the findings highlight the effectiveness of co-evolutionary algorithms in feature selection, particularly in the classification of spam emails, showcasing their potential for broader applications in text classification tasks. the paper contributes to the understanding of feature selection methodologies and their practical implications in enhancing classification accuracy and efficiency in data processing tasks. © 2024 IEEE.

关键词： Adversarial machine learning

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