检索结果-内蒙古大学图书馆

Learning user-item paths for explainable recommendation

IFAC-PapersOnLine 2020年第5期53卷 436-440页

作者： Tongxuan Wang Xiaolong Zheng Saike He Zhu Zhang Desheng Dash Wu School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China & The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Shenzhen Artificial Intelligence and Data Science Institute (Longhua) Shenzhen China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Shenzhen Artificial Intelligence and Data Science Institute (Longhua) Shenzhen China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China Shenzhen Artificial Intelligence and Data Science Institute (Longhua) Shenzhen China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China and also with the Stockholm Business School Stockholm University SE-106 91 Stockholm Sweden

Knowledge graph based explainable recommendation system is a kind of personalized recommendation which uses side information to solve the reason why recommending an item. Previous study has not fully explored the connection between users and items in knowledge graph, especially the problem of overall semantic representation, and can not capture the high level semantic representation of the path, it is difficult for existing path-based methods to clarify the overall semantics of paths. Especially when the path contains similar entities but different relationship. In this paper, we propose a model named Meta-Path-based Explainable Recommendation system (MPERS) to represent the paths in the knowledge graph through the semantic information of entities and relationships, and distinguish the different contributions that different paths make to conduct users’ preference. The experimental study demonstrates the superiority of our method compared with the state-of-the-art ones

关键词： explainable recommendation knowledge graph deep neural networks

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GPO: Global plane optimization for fast and accurate monocular SLAM initialization

arXiv

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arXiv 2020年

作者： Du, Sicong Guo, Hengkai Chen, Yao Lin, Yilun Meng, Xiangbing Wen, Linfu Wang, Fei-Yue PA2R2T Group State Key Laboratory of Complex System Management and Control Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing10049 China ByteDance AI Lab Beijing100089 China

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time. Copyright © 2020, The Authors. All rights reserved.

关键词： Vision

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Real-world Robot Reaching Skill Learning Based on Deep Reinforcement Learning

Real-world Robot Reaching Skill Learning Based on Deep Reinf...

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第32届中国控制与决策会议

作者： Naijun Liu Tao Lu Yinghao Cai Rui Wang Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institutes of Advanced TechnologyChinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology of the Chinese Academy of Sciences

Traditional programming method can achieve certain manipulation tasks with the assumption that robot environment is known and ***,with robots gradually applied in more domains,robots often encounter working scenes which are complicated,unpredictable,and *** overcome the limitation of traditional programming method,in this paper,we apply deep reinforcement learning(DRL) method to train robot agent to obtain skill *** policy trained with DRL on real-world robot is time-consuming and costly,we propose a novel and simple learning paradigm with the aim of training physical robot ***,our method train a virtual agent in an simulated environment to reach random target position from random initial ***,virtual agent trajectory sequence obtained with the trained policy,is transformed to real-world robot command with coordinate transformation to control robot performing reaching *** show that the proposed method can obtain self-adaptive reaching policy with low training cost,which is of great benefits for developing intelligent and robust robot manipulation skill system.

关键词： Robot reaching skill learning deep reinforcement learning

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Airport Aviation Noise Prediction Based on an Optimized Neural Network

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Transactions of Nanjing University of Aeronautics and Astronautics 2021年第S01期38卷 32-39页

作者： MA Lina TIAN Yong WU Xiaoyong College of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106P.R.China State Key Laboratory of Air Traffic Management System and Technology Nanjing210007P.R.China Department of Operation and Control Hunan Air Travel Co.Ltd.Changsha 410100P.R.China

In order to alleviate noise pollution and improve the sustainability of airport operation,it is of great significance to develop an effective method to predict airport aviation noise. A three-layer neural network is constructed to gain computational simplicity and execution economy. With the preferred node number and transfer functions obtained in comparative tests,the constructed network is further optimized through the genetic algorithm for performance improvements in prediction. Results show that the proposed model in this paper is superior in accuracy and stability for airport aviation noise prediction,contributing to the assessment of future environmental impact and further improvement of operational sustainability for civil airports.

关键词： noise prediction neural network genetic algorithm sustainable air transport

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Cognitive Artificial Population system: Framework and Application

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IFAC-PapersOnLine 2020年第5期53卷 495-500页

作者： Zi Li Dan He Kuangshi Huang Shaoru Jiang Peijun Ye School of Public Administration Chongqing Technology and Business University Chongqing 400067 China China Population and Development Research Center Beijing 100081 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Agent-based social simulation has been comprehensively applied in the research of social and ecological systems. At its core is an artificial population, which endogenously drives the system evolution for particular applications, such as urban transportation, reginal economics, analysis of infectious disease transmission, and military simulation. In contrast with the previous population simulations where simple mathematical models are used to ‘reproduce’ actual demographic features, this paper proposes a self-evolutionary digital population system, named as Cognitive Artificial Population system (CAPS). At a more fine-grained level, CAPS focuses on the agent cognitive, reasoning and learning process in their surrounding environment, thus can exploit most advantages from cognitive computing and Artificial Intelligence. As a case study, Chinese population evolution is implemented using the proposed framework. Computational experiments indicate that CAPS is able to achieve good predicted population structures for real social systems.

关键词： Artificial Population system Agent-Based Social Simulation Cognitive Computing

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Can AI detect pain and express pain empathy? A review from emotion recognition and a human-centered AI perspective

arXiv

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arXiv 2021年

作者： Cao, Siqi Fu, Di Yang, Xu Wermter, Stefan Liu, Xun Wu, Haiyan CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Department of Psychology University of Chinese Academy of Sciences Beijing China Department of Experimental Psychology University of Oxford Oxford United Kingdom State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Department of Informatics University of Hamburg Hamburg Germany Centre for Cognitive and Brain Sciences Department of Psychology University of Macau Taipa China

Sensory and emotional experiences such as pain and empathy are essential for mental and physical health. Cognitive neuroscience has been working on revealing mechanisms underlying pain and empathy. Furthermore, as trending research areas, computational pain recognition and empathic artificial intelligence (AI) show progress and promise for healthcare or human-computer interaction. Although AI research has recently made it increasingly possible to create artificial systems with affective processing, most cognitive neuroscience and AI research do not jointly address the issues of empathy in AI and cognitive neuroscience. The main aim of this paper is to introduce key advances, cognitive challenges and technical barriers in computational pain recognition and the implementation of artificial empathy. Our discussion covers the following topics: How can AI recognize pain from unimodal and multimodal information? Is it crucial for AI to be empathic? What are the benefits and challenges of empathic AI? Despite some consensus on the importance of AI, including empathic recognition and responses, we also highlight future challenges for artificial empathy and possible paths from interdisciplinary perspectives. Furthermore, we discuss challenges for responsible evaluation of cognitive methods and computational techniques and show approaches to future work to contribute to affective assistants capable of empathy. © 2021, CC BY-NC-ND.

关键词： Cognitive systems

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Multi-Agent Reinforcement Learning Based on Clustering in Two-Player Games

Multi-Agent Reinforcement Learning Based on Clustering in Tw...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Weifan Li Yuanheng Zhuand Dongbin Zhao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing China

ISBN: (数字)9781728124858

ISBN: (纸本)9781728124865

Non-stationary environment is general in real environment, including adversarial environment and multi-agent problem. Multi-agent environment is a typical non-stationary environment. Each agent of the shared environment must learn a efficient interaction for maximizing the expected reward. Independent reinforcement learning (InRL) is the simplest form in which each agent treats other agents as part of environment. In this paper, we present Max-Mean-Learning-Win-or-Learn-Fast (MML-WoLF), which is an independent on-policy learning algorithm based on reinforcement clustering. A variational auto-encoder method based on reinforcement learning is proposed to extract features for unsupervised clustering. Based on clustering results, MML-WoLF uses statistics and the dominated factor to calculate the values of the states that belong to a certain category. The agent policy is iteratively updated by the value. We apply our algorithm to multi-agent problems including matrix-game, grid world, and continuous world game. The clustering results are able to show the strategies distribution under the agent's current policy. The experiment results suggest that our method significantly improves average performance over other independent learning algorithms in multi-agent problems.

关键词： Games Clustering algorithms Learning (artificial intelligence) Probability distribution Nash equilibrium Feature extraction Approximation algorithms

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Parallel Planning:A New Motion Planning Framework for Autonomous Driving

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IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

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An Obstacles Avoidance Algorithm Based on Improved Artificial Potential Field

An Obstacles Avoidance Algorithm Based on Improved Artificia...

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IEEE International Conference on Mechatronics and Automation

作者： Si Xi Lu En Li Rui Guo School of Artificial Intelligence University of Chinese Academy of Sciences No.19(A) Yuquan Road Beijing China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science 95 East ZhongGuanCun Road Beijing China State Grid Shandong Electric Power Company Jinan China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Obstacle avoidance is one of the most important issue in the motion planning and control of robot. There are many algorithms to avoid obstacles, but obstacle avoidance algorithm for manipulator is needed consider not only end effector, but also all links of manipulator. In terms of real-time and speed of all system, the algorithm also should be easy as possible. In this paper, an algorithm based on the improved artificial potential field is proposed to solve the problem of the local optimum. The idea of improvement is to find a path by fitting curve with target point and critical oscillating points. There is also a comparison in this paper with planning a path by tangent line. The conclusion proves that the path is much smoother by the algorithm based on improved artificial potential field in this paper. Furthermore, the problem of judder on the trajectory of traditional artificial potential field is also solved in this paper by an improved repulsive potential field function. Finally, the algorithm is proved by planning a smooth path for obstacle avoidance on manipulator: KinovaGen3 in MATLAB.

关键词： Mechatronics Fitting End effectors Real-time systems Planning Trajectory Collision avoidance

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Development of GPU Enhanced AI Education Platforms for K-12 Schools

Development of GPU Enhanced AI Education Platforms for K-12 ...

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Chinese Automation Congress (CAC)

作者： Xiaoyan Gong Yifan Wen Xiwei Liu Hongxia Zhao Sifeng Jing Qiang Li Kai Gao Jilong Luo The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao China The 21st Century International School Beijing China Beijing NO. 2 Middle School Beijing China Beijing University of Chemical Technology Beijing China

ISBN: (纸本)9781665426480

Recently, with continuous implementation of education reform, AI (Artificial Intelligence) has been included in K-12 (Kindergarten to 12th grade) curricula and AI education is in full swing in China. However, standard, cost-efficient AI education platforms for K-12 schools are still not available. So in this paper, based on Jetson Nano, we developed a GPU-enhanced AI teaching platform including AI learning computers for students to develop AI-related algorithms and applications, AI computer clusters for whole classes, and modular auto-driving cars to run and test algorithms and applications.

关键词： Computers Automation Education Clustering algorithms Graphics processing units Automobiles Artificial intelligence

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