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Recent Progress in Reinforcement Learning and Adaptive Dynamic Programming for Advanced Control Applications

IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 18-36页

作者： Ding Wang Ning Gao Derong Liu Jinna Li Frank L.Lewis IEEE the Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent SystemBeijing Laboratory of Smart Environmental Protectionand Beijing Institute of Artificial IntelligenceBeijing University of TechnologyBeijing 100124China the School of System Design and Intelligent Manufacturing Southern University of Science and TechnologyShenzhen 518055China the Department of Electrical and Computer Engineering University of Illinois at ChicagoChicago IL 60607 USA the School of Information and Control Engineering Liaoning Petrochemical UniversityFushun 113001China the UTA Research Institute the University of Texas at ArlingtonArlington TX 76118 USA

Reinforcement learning(RL) has roots in dynamic programming and it is called adaptive/approximate dynamic programming(ADP) within the control community. This paper reviews recent developments in ADP along with RL and its applications to various advanced control fields. First, the background of the development of ADP is described, emphasizing the significance of regulation and tracking control problems. Some effective offline and online algorithms for ADP/adaptive critic control are displayed, where the main results towards discrete-time systems and continuous-time systems are surveyed, ***, the research progress on adaptive critic control based on the event-triggered framework and under uncertain environment is discussed, respectively, where event-based design, robust stabilization, and game design are reviewed. Moreover, the extensions of ADP for addressing control problems under complex environment attract enormous attention. The ADP architecture is revisited under the perspective of data-driven and RL frameworks,showing how they promote ADP formulation ***, several typical control applications with respect to RL and ADP are summarized, particularly in the fields of wastewater treatment processes and power systems, followed by some general prospects for future research. Overall, the comprehensive survey on ADP and RL for advanced control applications has d emonstrated its remarkable potential within the artificial intelligence era. In addition, it also plays a vital role in promoting environmental protection and industrial intelligence.

关键词： Adaptive dynamic programming(ADP) advanced control complex environment data-driven control event-triggered design intelligent control neural networks nonlinear systems optimal control reinforcement learning(RL)

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Towards Practical Finite Sample Bounds for Motion Planning in TAMP

arXiv

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arXiv 2024年

作者： Shaw, Seiji Curtis, Aidan Kaelbling, Leslie Pack Lozano-Pérez, Tomás Roy, Nicholas Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology CambridgeMA United States

When using sampling-based motion planners such as PRMs, it is difficult to determine how many samples are required for the PRM to find a solution consistently. This is particularly relevant in Task and Motion Planning (TAMP), where many motion planning problems must be solved sequentially. We attempt to address this problem by proving an upper bound on the number of samples that are sufficient, with high probability, for a radius PRM to find a feasible solution, drawing on prior work in deterministic sampling and sample complexity theory. We also introduce a numerical algorithm that refines the bound based on the proofs of the sample complexity results we leverage. Our experiments show that our numerical algorithm is tight up to two to three orders of magnitude on planar problems for radius PRMs but becomes looser as the problem’s dimensionality increases. The numerical algorithm is empirically more useful as a heuristic for estimating the number of samples needed for a KNN PRM in low dimensions. When deployed to schedule samples for a KNN PRM in a TAMP planner, we also observe improvements in planning time in planar problems. While our experiments show that much work remains to tighten our bounds, the ideas presented in this paper are a step towards a practical sample bound. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

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Logical Relation Modeling and Mining in Hyperbolic Space for Recommendation 40

Logical Relation Modeling and Mining in Hyperbolic Space for...

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40th IEEE International Conference on Data Engineering, ICDE 2024

作者： Tan, Yanchao Lv, Hang Zhou, Zihao Guo, Wenzhong Xiong, Bo Liu, Weiming Chen, Chaochao Wang, Shiping Yang, Carl College of Computer and Data Science Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Ministry of Education Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Institute for Artificial Intelligence University of Stuttgart Stuttgart Germany College of Computer Science Zhejiang University Hangzhou China Emory University Department of Computer Science Atlanta United States

ISBN: (纸本)9798350317152

The sparse interactions between users and items have aggravated the difficulty of their representations in recommender systems. Existing methods leverage tags to alleviate the sparsity problem but ignore prevalent logical relations among items and tags (e.g., membership, hierarchy, and exclusion), which can be leveraged to enhance the accuracy of modeling user preferences and conducting recommendations. To this end, we propose to extract logical relations among item tags from existing tag taxonomies and exploit the individual strengths of the Poincaré and the Lorentz models in hyperbolic space for logical relation modeling towards enhanced recommendations. Moreover, we find that the logical relations directly extracted from existing tag taxonomies can be inaccurate and coarse. Therefore, we further devise innovative consistency-based and granularity-based weighting mechanisms based on user behavior patterns for data-driven logical relation mining that can be jointly optimized along with recommendations in an end-to-end fashion. Extensive experiments on four real-world benchmark datasets show drastic performance gains brought by our proposed framework, which constantly achieves an average of 8.25% improvement over state-of-the-art competitors regarding both Recall and NDCG metrics. Insightful case studies further demonstrate that our automatically refined logical relations are highly accurate and interpretable. © 2024 IEEE.

关键词： Recommender systems

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Modified simulated annealing for university teacher course assignment considering socio-cultural constraints

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1533-1549页

作者： Brahimi, Samiha Sabba, Sara Elhussein, Mariam Alqahtani, Mohammed Department of Computer Information Systems College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Faculty of New Technologies of Information and Communication Laboratory of Data Science and Artificial Intelligence (LISIA) Abdelhamid Mahri Constantine-2 University Constantine 25016 Algeria Department of Mathematics and Computing Mount Royal University Calgary AB Canada

This paper aims to address the complex challenge of course assignment for faculty members within a Saudi university, taking into account the socio-cultural constraints imposed by gender-based segregation between students and faculty. To tackle this challenge, a combinatorial optimization model is proposed, which includes a binary representation of the problem, constraints reflecting gender segregation, and an objective function designed to maximize faculty satisfaction. The model employs a Simulated Annealing (SA) algorithm, supplemented with two neighborhood search heuristics (H1 and H2), to explore diverse combinations. Additionally, a greedy randomized adaptive search procedure Greedy Randomized Adaptive Search Procedure (GRASP) algorithm is implemented for comparative analysis, utilizing heuristic 1 for local search. Through the application of simulated annealing alongside both heuristics (SA_H1_H2), the optimal solution is achieved, resulting in a notable equilibrium of courses among faculty members. Objective 1 demonstrates the standardized deviation in workload distribution among faculty members, yielding a deviation value of 0.08. This study contributes to the field by proposing a novel approach to course assignment that explicitly addresses the gender-based segregation prevalent in Saudi universities. By integrating socio-cultural constraints into the optimization model and employing advanced algorithms, the study offers a pioneering solution to a previously unexplored problem domain. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： GRASP Mathematical model Operations Research Optimization Simulated annealing Teacher_Course assignment

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Profit-based uncertainty estimation with application to credit scoring

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European Journal of Operational Research 2025年第2期325卷 303-316页

作者： Xu, Yong Kou, Gang Ergu, Daji College of Computer Science and Artificial Intelligence Southwest Minzu University Chengdu610225 China College of Electronic and Information Southwest Minzu University Chengdu610225 China Key Laboratory of Electronic Information Engineering Southwest Minzu University Chengdu610225 China School of Business Administration Faculty of Business Administration Southwestern University of Finance and Economics Chengdu611130 China Xiangjiang Laboratory Changsha410205 China

Credit scoring is pivotal in financial risk management and has attracted significant research interest. While existing studies primarily concentrate on enhancing model predictive power and economic value, they often overlook the crucial aspect of predictive uncertainty, especially in the context of deep neural networks applied to credit scoring. This study addresses uncertainty estimation in credit scoring and evaluates three widely used uncertainty methods across various credit datasets. Additionally, guided by the maximum profit criterion, we propose two profit-based uncertainty metrics to assess profit uncertainties stemming from predictive uncertainty, specifically targeting class-dependent and instance-dependent cost scenarios. Subsequently, we develop a classification system with a rejection mechanism based on these metrics. Our approach aims to improve model profitability and reduce predictive uncertainty, specifically regarding model profit. Empirical results across several benchmark credit datasets indicate that our proposed framework outperforms existing methods in terms of increasing model profit in different credit-scoring scenarios. Furthermore, sensitivity analyses of varying cost parameter settings highlight the robustness of our framework. © 2025 The Authors

关键词： Decision support systems Uncertainty estimation Performance measurement Deep learning Credit scoring

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Generating Editable Head Avatars with 3D Gaussian GANs

Generating Editable Head Avatars with 3D Gaussian GANs

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Guohao Li Hongyu Yang Yifang Men Di Huang Weixin Li Ruijie Yang Yunhong Wang School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generating animatable and editable 3D head avatars is essential for various applications in computer vision and graphics. Traditional 3D-aware generative adversarial networks (GANs), often using implicit fields like Neural Radiance Fields (NeRF), achieve photo-realistic and view-consistent 3D head synthesis. However, these methods face limitations in deformation flexibility and editability, hindering the creation of lifelike and easily modifiable 3D heads. We propose a novel approach that enhances the editability and animation control of 3D head avatars by incorporating 3D Gaussian Splatting (3DGS) as an explicit 3D representation. This method enables easier illumination control and improved editability. Central to our approach is the Editable Gaussian Head (EG-Head) model, which combines a 3D Morphable Model (3DMM) with texture maps, allowing precise expression control and flexible texture editing for accurate animation while preserving identity. To capture complex non-facial geometries like hair, we use an auxiliary set of 3DGS and tri-plane features. Extensive experiments demonstrate that our approach delivers high-quality 3D-aware synthesis with state-of-the-art controllability. Our code and models are available at https://***/liguohao96/EGG3D.

关键词： Training Solid modeling Three-dimensional displays Head Accuracy Avatars Superresolution Animation Neural radiance field Generative adversarial networks

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Quantifying and Mitigating Unimodal Biases in Multimodal Large Language Models: A Causal Perspective

arXiv

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arXiv 2024年

作者： Chen, Meiqi Cao, Yixin Zhang, Yan Lu, Chaochao State Key Laboratory of General Artificial Intelligence Peking University Beijing China School of Intelligence Science and Technology Peking University China School of Computer Science Fudan University China Shanghai Artificial Intelligence Laboratory China

Recent advancements in Large Language Models (LLMs) have facilitated the development of Multimodal LLMs (MLLMs). Despite their impressive capabilities, MLLMs often suffer from over-reliance on unimodal biases (e.g., language bias and vision bias), leading to incorrect answers or hallucinations in complex multimodal tasks. To investigate this issue, we propose a causal framework to interpret the biases in Visual Question Answering (VQA) problems. Within this framework, we conduct an in-depth causal analysis to assess the causal effect of these biases on MLLM predictions. Based on the analysis, we introduce 1) a novel MORE dataset with 12,000 challenging VQA instances requiring multi-hop reasoning and overcoming unimodal biases. 2) a causality-enhanced agent framework CAVE that guides models to comprehensively integrate information from different modalities and mitigate biases. Our experiments show that MLLMs perform poorly on MORE, indicating strong unimodal biases and limited semantic understanding. However, when integrated with our CAVE, promising improvements in reasoning and bias mitigation can be seen. These findings provide important insights for the development of more robust MLLMs and contribute to the broader goal of advancing multimodal AI systems capable of deeper understanding and reasoning. Our project page is at https://***/OpenCausaLab/MORE. Copyright © 2024, The Authors. All rights reserved.

关键词： Semantics

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Learning Robust Global-Local Representation From EEG for Neural Epilepsy Detection

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2024年第11期5卷 5720-5732页

作者： Zhou, Xinliang Liu, Chenyu Yang, Ruizhi Zhang, Liangwei Zhai, Liming Jia, Ziyu Liu, Yang Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Queen Mary University of London School of Electronic Engineering and Computer Science LondonE1 4NS United Kingdom Carnegie Mellon University School of Computer Science PittsburghPA15213 United States Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Wuhan430079 China Central China Normal University School of Computer Science Wuhan430079 China Chinese Academy of Sciences Brainnetome Center Institute of Automation Beijing100190 China

Epilepsy is a life-threatening and challenging neurological disorder, and applying an electroencephalogram (EEG) is a commonly used clinical approach for its detection. Neuropsychological research indicates that epilepsy seizures are highly associated with distinct ranges of temporal EEG patterns. Although previous attempts to automatically detect epilepsy have achieved high classification performance, one crucial challenge still remains: how to effectively learn the robust global-local representation associated with epilepsy in the signals? To address the above challenge, we propose global-local neural epilepsy detection network (GlepNet), a novel architecture for automatic EEG epilepsy detection. We interleave the temporal convolution model together with the multihead attention mechanism within the GlepNet's encoder blocks to jointly capture the interlaced epilepsy seizure local and global features in EEG signals. Meanwhile, the interpretable method, gradient-weighted class activation mapping (Grad-CAM), is applied to visually confirm that the GlepNet acquires the ability to accord significant weight to EEG segments containing epileptiform abnormalities such as spike-wave complexes. Specifically, the Grad-CAM heatmaps are generated by backpropagating the gradients from the encoder blocks to highlight the epilepsy seizure-related parts. Extensive experiments show the superiority of the GlepNet over state-of-the-art methods on multiple EEG epilepsy datasets. The code will soon be open-sourced on GitHub. © 2024 IEEE.

关键词： Feature extraction

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3D2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for Realistic Gaussian Avatar Modeling 39

3D2-Actor: Learning Pose-Conditioned 3D-Aware Denoiser for R...

引用

39th Annual AAAI Conference on artificial intelligence, AAAI 2025

作者： Tang, Zichen Yang, Hongyu Zhang, Hanchen Chen, Jiaxin Huang, Di School of Artificial Intelligence Beihang University Beijing China Shanghai Artificial Intelligence Laboratory Shanghai China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)157735897X

Advancements in neural implicit representations and differentiable rendering have markedly improved the ability to learn animatable 3D avatars from sparse multi-view RGB videos. However, current methods that map observation space to canonical space often face challenges in capturing pose-dependent details and generalizing to novel poses. While diffusion models have demonstrated remarkable zero-shot capabilities in 2D image generation, their potential for creating animatable 3D avatars from 2D inputs remains underexplored. In this work, we introduce 3D2-Actor, a novel approach featuring a pose-conditioned 3D-aware human modeling pipeline that integrates iterative 2D denoising and 3D rectifying steps. The 2D denoiser, guided by pose cues, generates detailed multi-view images that provide the rich feature set necessary for high-fidelity 3D reconstruction and pose rendering. Complementing this, our Gaussian-based 3D rectifier renders images with enhanced 3D consistency through a two-stage projection strategy and a novel local coordinate representation. Additionally, we propose an innovative sampling strategy to ensure smooth temporal continuity across frames in video synthesis. Our method effectively addresses the limitations of traditional numerical solutions in handling ill-posed mappings, producing realistic and animatable 3D human avatars. Experimental results demonstrate that 3D2-Actor excels in high-fidelity avatar modeling and robustly generalizes to novel poses. Copyright © 2025, Association for the Advancement of artificial intelligence (***). All rights reserved.

关键词： Rendering (computer graphics)

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Research on Visual Slam Technology for UAV Positioning and Navigation 21

Research on Visual Slam Technology for UAV Positioning and N...

引用

21st International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2024

作者： Wang, Zhou Zhihao, Liu Ying, Tian Yujun, Yang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China School of Computer and Software Engineering Xihua University Chengdu China Sichuan Police College Intelligent Policing Key Laboratory of Sichuan Province LuZhou China College of Computer and Artificial Intelligence Software College Huaihua University Huaihua China

ISBN: (纸本)9798331519254

UAV (Unmanned Aerial Vehicle) navigation can be considered as the process of robots that determine how to successfully and quickly reach the target location. Specifically, in order to complete the scheduled task successfully, the UAV must accurately understand its status, such as position, navigation speed, heading, as well as the starting point and target position. Moreover, it mainly depends on the current environment and location. This article generally focuses on the research on visual SLAM (Simultaneous localization and mapping) for UAV positioning and navigation. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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