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Optimal synchronization control formulti-agent systems with input saturation:a nonzero-sum game

Frontiers of Information Technology & Electronic Engineering 2022年第7期23卷 1010-1019页

作者： Hongyang LI Qinglai WEI School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

This paper presents a novel optimal synchronization control method for multi-agent systems with input *** multi-agent game theory is introduced to transform the optimal synchronization control problem into a multi-agent nonzero-sum ***,the Nash equilibrium can be achieved by solving the coupled Hamilton–Jacobi–Bellman(HJB)equations with nonquadratic input energy terms.A novel off-policy reinforcement learning method is presented to obtain the Nash equilibrium solution without the system models,and the critic neural networks(NNs)and actor NNs are introduced to implement the presented *** analysis is provided,which shows that the iterative control laws converge to the Nash *** results show the good performance of the presented method.

关键词： Optimal synchronization control Multi-agent systems Nonzero-sum game Adaptive dynamic programming Input saturation Off-policy reinforcement learning Policy iteration

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The DAO to Social Transportation: Towards Smart Mobility in Cyber-Physical-Social Space

The DAO to Social Transportation: Towards Smart Mobility in ...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuanyuan Chen Yisheng Lv Fei-Yue Wang State Key Lab. of Multimodal Artificial Intelligence Systems Chinese Academy of Sciences Institute of Automation Beijing China State Key Lab. for Management and Control of Complex Systems Chinese Academy of Sciences Beijing China

The rapid advancement of intelligence and connectivity technology (ICT) has enhanced the efficiency and safety of intelligent transportation systems (ITS). However, this also increases the complexity of the transportation systems, especially the social complexity, and poses new challenges for their management. This paper discusses the emergence of social transportation, as part of natural evolutionary adaptation to new traffic conditions and technology, which is a paradigm shift from engineering-centered technical systems to society-centered ecosystems. In social transportation systems, all participants including administrators, individual travelers and other stakeholders play a more proactive role in the operation and management of transportation systems. Therefore, we propose to employ a decen-tralized autonomous organization (DAO) as a model for the social transportation system that allows participants to collaborate and coordinate without relying on a central authority. To meet the challenges brought by this transition, we propose to apply ACP-based parallel system theory to restructure the methodology of transportation management. Finally, we present a case study of personal carbon trading system where economic incentives are used to manage traffic demand, affecting others' travel behaviors, and reduce carbon emissions. In the conclusive remarks, we provide our visions and future directions of social transportation systems.

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Multilingual Knowledge Graph Completion from Pretrained Language Models with Knowledge Constraints

arXiv

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arXiv 2024年

作者： Song, Ran He, Shizhu Gao, Shengxiang Cai, Li Liu, Kang Yu, Zhengtao Zhao, Jun Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Yunnan Key Laboratory of Artificial Intelligence Kunming China The Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Science Beijing China Meituan Beijing China

Multilingual Knowledge Graph Completion (mKGC) aim at solving queries like (h, r, ?) in different languages by reasoning a tail entity t thus improving multilingual knowledge graphs. Previous studies leverage multilingual pretrained language models (PLMs) and the generative paradigm to achieve mKGC. Although multilingual pretrained language models contain extensive knowledge of different languages, its pretraining tasks cannot be directly aligned with the mKGC tasks. Moreover, the majority of KGs and PLMs currently available exhibit a pronounced English-centric bias. This makes it difficult for mKGC to achieve good results, particularly in the context of low-resource languages. To overcome previous problems, this paper introduces global and local knowledge constraints for mKGC. The former is used to constrain the reasoning of answer entities, while the latter is used to enhance the representation of query contexts. The proposed method makes the pretrained model better adapt to the mKGC task. Experimental results on public datasets demonstrate that our method outperforms the previous SOTA on Hits@1 and Hits@10 by an average of 12.32% and 16.03%, which indicates that our proposed method has significant enhancement on mKGC. © 2024, CC BY.

关键词： Knowledge graph

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Modeling and analysis of an underwater biomimetic vehicle-manipulator system

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science China(Information sciences) 2022年第3期65卷 255-257页

作者： Xuejian BAI Yu WANG Shuo WANG Rui WANG Min TAN Wei WANG School of Artificial Intelligence University of Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology School of Control Science and Engineering Shandong University

In recent years, underwater vehicle-manipulator systems(UVMSs) have captured researchers' attention. Inspired by fish, bionic mechanisms were introduced into a *** underwater biomimetic vehicle-manipulator system(UBVMS) is actuated by two symmetrically arranged biomimetic undulatory fin propulsors [1]. Based on the UBVMS, some topics have been researched, such as freefloating autonomous operation [2, 3], three-dimensional helical path following [4], and underwater video processing [5].

关键词： underwater biomimetic vehicle underwater robots underwater vehicle-manipulator system biomimetic robots hydrodynamic modeling

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Current opinions on large cellular models

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Quantitative Biology 2024年第4期12卷 433-443页

作者： Minsheng Hao Lei Wei Fan Yang Jianhua Yao Christina V.Theodoris Bo Wang Xin Li Ge Yang Xuegong Zhang MOE Key Laboratory of Bioinformatics and Bioinformatics Division BNRISTDepartment of AutomationTsinghua UniversityBeijingChina AI Lab TencentShenzhenChina Gladstone Institutes and University of California San FranciscoCaliforniaUSA Department of Computer Science University of TorontoTorontoOntarioCanada State Key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijingChina State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Life Sciences and School of Medicine Center for Synthetic and Systems BiologyTsinghua UniversityBeijingChina

1|*** language models(LLMs)have made breakthroughs in natural language processing(NLP)and understanding,and have brought revolutions in many other fields[1-4].Inspired by those successes,several large cellular models(LCMs)adopting similar structures of LLMs have been developed for single-cell transcriptomics,including(but not limited to)scBERT[5],Geneformer[6],scGPT[7],scFoundation[8],and GeneCompass[9].The practices of these models have shown LCMs’power and potential in various biological tasks and illustrated the possibilities of revolutionizing future biological studies by LCMs.

关键词： large cellular models large language models scBERT Geneformer scGPT scFoundation GeneCompass single-cell transcriptomics

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AI4S Based on DeSci: Reference Model and Research Issues

AI4S Based on DeSci: Reference Model and Research Issues

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IEEE International Conference on systems, Man and Cybernetics

作者： Wenwen Ding Juanjuan Li Rui Qin Sangtian Guan Fei-Yue Wang Faculty of Innovation Engineering Macau University of Science and Technology Macau China The State Key Laboratory for Management and Control of Complex Systems The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

The rise of Artificial intelligence for science (AI4S) has highlighted the importance and urgency of ensuring open-ness, fairness, impartiality, diversity, and sustainability in scientific systems. Existing scientific systems, referred to as Centralized science (CeSci), are built on centralized organizational structures and top-down institutional frameworks, which are lagging behind the development and practical requirements of AI4S. To address these limitations, AI4S needs to embrace a new scientific organizational and operational paradigm, namely Decentralized science (DeSci). It can provide strong support to AI4S via effectively addressing issues such as information silos, biases, unfair distribution, and monopolies and promoting multidisciplinary, interdisciplinary, and trans disciplinary cooperation in science. Based on these considerations, this paper presents the framework of AI4S based on DeSci and explores its potential application scenarios and research issues. The research can provide effective guidance for the development of scientific systems.

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A Novel Two-Stream Model for Human Motor Characteristics Learning

A Novel Two-Stream Model for Human Motor Characteristics Lea...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xiang, Tian-Yu Zhou, Xiao-Hu Xie, Xiao-Liang Liu, Shi-Qi Yang, Hong-Jun Feng, Zhen-Qiu Hao, Jian-Long Hou, Zeng-Guang Institute of Automation The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of Sciences Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China Institute of Systems Engineering Macau University of Science and Technology The Joint Laboratory of Intelligence Science and Technology Taipa China

ISBN: (纸本)9781665481090

Electroencephalogram (EEG) is one of the most pop-ular sources for learning human motor characteristics. However, most current methods only consider its temporal features and overlook spatial information. This paper proposes a novel two-stream model based on spatial and temporal features to learn human motor characteristics from EEG signals. The spatial stream is analyzed by graph neural networks (GNN) to extract the relationship among different channels. Adjacent matrices can be learned through the training process, unlike pre-defined ones used in other GNN-based EEG analysis methods. The temporal stream is processed with mixed convolution structures, which consist ID convolution, depthwise convolution, and separable convolution. The features of two streams are fused with fully-connected neural networks for human motor characteristics learning. Experimental results show the proposed model can achieve the accuracy of 0.684/0.709 (four classes motor imagery) in subject-independent/dependent schemes, demonstrating the promising for it used in brain computer interface (BCI) scenarios. © 2022 IEEE.

关键词： Convolution

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A Novel Heterogeneous Actor-critic Algorithm with Recent Emphasizing Replay Memory

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International Journal of automation and computing 2021年第4期18卷 619-631页

作者： Bao Xi Rui Wang Ying-Hao Cai Tao Lu Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200031China

Reinforcement learning(RL) algorithms have been demonstrated to solve a variety of continuous control tasks. However,the training efficiency and performance of such methods limit further applications. In this paper, we propose an off-policy heterogeneous actor-critic(HAC) algorithm, which contains soft Q-function and ordinary Q-function. The soft Q-function encourages the exploration of a Gaussian policy, and the ordinary Q-function optimizes the mean of the Gaussian policy to improve the training efficiency. Experience replay memory is another vital component of off-policy RL methods. We propose a new sampling technique that emphasizes recently experienced transitions to boost the policy training. Besides, we integrate HAC with hindsight experience replay(HER) to deal with sparse reward tasks, which are common in the robotic manipulation domain. Finally, we evaluate our methods on a series of continuous control benchmark tasks and robotic manipulation tasks. The experimental results show that our method outperforms prior state-of-the-art methods in terms of training efficiency and performance, which validates the effectiveness of our method.

关键词： Reinforcement learning(RL) actor-critic experience replay training efficiency manipulation skill learning

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Tree-of-Code: A Tree-Structured Exploring Framework for End-to-End Code Generation and Execution in complex Task Handling

arXiv

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arXiv 2024年

作者： Ni, Ziyi Li, Yifan Yang, Ning Shen, Dou Lv, Pin Dong, Daxiang The Key Laboratory of Cognition and Decision Intelligence for Complex Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Science China Baidu Inc. China Global Innovation Exchange Institution Tsinghua University China

Solving complex reasoning tasks is a key real-world application of agents. Thanks to the pretraining of Large Language Models (LLMs) on code data, recent approaches like CodeAct successfully use code as LLM agents’ action, achieving good results. However, CodeAct greedily generates the next action’s code block by relying on fragmented thoughts, resulting in inconsistency and instability. Moreover, CodeAct lacks action-related ground-truth (GT), making its supervision signals and termination conditions questionable in multi-turn interactions. To address these issues, we first introduce a simple yet effective end-to-end code generation paradigm, CodeProgram, which leverages code’s systematic logic to align with global reasoning and enable cohesive problem-solving. Then, we propose Tree-of-Code (ToC), which self-grows CodeProgram nodes based on the executable nature of the code and enables self-supervision in a GT-free scenario. Experimental results on two datasets using ten popular zero-shot LLMs show ToC remarkably boosts accuracy by nearly 20% over CodeAct with less than 1/4 turns. Several LLMs even perform better on one-turn CodeProgram than on multi-turn CodeAct. To further investigate the trade-off between efficacy and efficiency, we test different ToC tree sizes and exploration mechanisms. We also highlight the potential of ToC’s end-to-end data generation for supervised and reinforced fine-tuning. © 2024, CC BY-NC-SA.

关键词： Modeling languages

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Consensus Control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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