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Learning Cooperation and Online Planning Through Simulation and Graph Convolutional Network [article]

Rafid Ameer Mahmud, Fahim Faisal, Saaduddin Mahmud, Md. Mosaddek Khan
2021 arXiv   pre-print
Specifically, SiCLOP tailors Monte Carlo Tree Search (MCTS) and uses Coordination Graph (CG) and Graph Neural Network (GCN) to learn cooperation and provides real time solution of a MMDP problem.  ...  Against this background, we introduce a simulation based online planning algorithm, that we call SiCLOP, for multi-agent cooperative environments.  ...  Graph Convolutional Networks (GCN) is a popular GNN for graph embedding.  ... 
arXiv:2110.08480v1 fatcat:3rw4kdve6zgldpwkmmbosov2x4

Graph Neural Networks for Decentralized Multi-Robot Path Planning [article]

Qingbiao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok
2020 arXiv   pre-print
Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots  ...  We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations.  ...  This framework is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that learns to explicitly communicate these  ... 
arXiv:1912.06095v2 fatcat:geer45ylknextjb3objf3kek74

Graph Neural Networks for Decentralized Multi-Robot Path Planning

Qingbiao Li, Fernando Gama, Alejandro Ribeiro, Amanda Prorok
2020 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)  
Our architecture is composed of a convolutional neural network (CNN) that extracts adequate features from local observations, and a graph neural network (GNN) that communicates these features among robots  ...  We train the model to imitate an expert algorithm, and use the resulting model online in decentralized planning involving only local communication and local observations.  ...  We also thank Binyu Wang and Zhe Liu for their help in providing benchmark algorithms.  ... 
doi:10.1109/iros45743.2020.9341668 fatcat:u4a7mj3fwjdnnggpg3z3bsh7ee

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
This is a two-stage processing framework composed of a region proposal network and a convolutional capsule network.  ...  Then, the convolutional capsule network classifies these region proposals into the background and different types of vehicle logos.  ...  Optimized Graph Convolution Recurrent Neural Network for Traffic Prediction K. Guo, Y. Hu, Z. Qian, H. Liu, K. Zhang, Y. Sun, J. Gao, and B.  ... 
doi:10.1109/tits.2021.3052540 fatcat:wvccn3i32jdaxoov6mibk2tlku

Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning

Ralvi Isufaj, Marsel Omeri, Miquel Angel Piera
2022 Applied Sciences  
We implement an algorithm based on graph neural networks where cooperative agents can communicate to jointly generate resolution maneuvers.  ...  The model is evaluated in scenarios with 3 and 4 present agents. Results show that agents are able to successfully solve the multi-UAV conflicts through a cooperative strategy.  ...  Variants include: Graph Attention Networks (GATs) [39], Graph Convolutional Networks (GCNs) [40] and Message Passing Neural Networks (MPNNs) [41].  ... 
doi:10.3390/app12020610 fatcat:zv74eywngfh53h2chyxk4y4vke

2020 Index IEEE Transactions on Intelligent Transportation Systems Vol. 21

2020 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS Sept. 2020 3848-3858 Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.  ...  ., +, TITS Sept. 2020 3848-3858 Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting.  ... 
doi:10.1109/tits.2020.3048827 fatcat:ab6he3jkfjboxg7wa6pagbggs4

Analysis of the Business Model of C2B Cross-Border E-Commerce Platform Based on Deep Learning

Jun He, Jian Su
2021 Security and Communication Networks  
First, it has general understanding of related theories, then uses the deep learning model to analyze the business models of cross-border EC, and finally analyzes the results.  ...  Among them, the trends of active users and costs are similar, and the trends of orders and revenue are similar.  ...  studying deep learning is to establish a neural network simulating the human brain for analytical learning.  ... 
doi:10.1155/2021/9025986 fatcat:kcqaajatxjfblonsnyqykstdvq

Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
LMI-Based Synthesis of String-Stable Controller for Cooperative Adaptive Cruise Control Y. Zhu, H. He, and D.  ...  In addition, extensive model-in-the-loop simulations are carried out and the results are compared with driver-inthe-loop experiments. The simulation results indicated that  ...  Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting Z. Cui, K. Henrickson, R. Ke, and Y.  ... 
doi:10.1109/tits.2020.3031998 fatcat:bkprv5oa25e3zhoj23nbrprj6e

Scanning the Issue

Azim Eskandarian
2022 IEEE transactions on intelligent transportation systems (Print)  
Yoon A novel deep learning model, multi-weight traffic graph convolutional (MW-TGC) network is proposed to solve the traffic prediction problem in the urban environment.  ...  Incorporating Dynamicity of Transportation Network With Multi-Weight Traffic Graph Convolutional Network for Traffic Forecasting Y. Shin and Y.  ... 
doi:10.1109/tits.2022.3152066 fatcat:szsznss35fc7rf43edw6xsibya

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network [article]

Maosen Li, Siheng Chen, Yanning Shen, Genjia Liu, Ivor W. Tsang, Ya Zhang
2021 arXiv   pre-print
Extensive experiments are conducted on three tasks: online simulated trajectory prediction, online human motion prediction and online traffic speed prediction, and our methods outperform state-of-the-art  ...  This theoretical interpretability distinguishes our method from many other graph networks.  ...  We illustrate the adjacency matrices of both simulated graphs and learned graphs.  ... 
arXiv:2107.00894v1 fatcat:4djnqe722vfx5d3xzn6oyig2by

Scanning the Issue

Azim Eskandarian
2020 IEEE transactions on intelligent transportation systems (Print)  
To evaluate the ACF model, they performed the simulation for car-following pairs and conducted a comparison analysis with the existing models: Newell, Gipps, GM, and IDM.  ...  The datasets repository is available at: https://sites.google.com/view/driveability-survey-dataset Deep Learning for Intelligent Transportation Systems: A Survey of Emerging Trends M. Veres and M.  ...  A Collaborative Visual Tracking Architecture for Correlation Filter and Convolutional Neural Network Learning A Rule-Based Cooperative Merging Strategy for Connected and Automated Vehicles J.  ... 
doi:10.1109/tits.2020.3008809 fatcat:etol5qoilvdnbj6gtjxk3gheaa

Scanning the Issue

Azim Eskandarian
2022 IEEE transactions on intelligent transportation systems (Print)  
In total, 90 subjects from China, South Korea, and the USA assume a pedestrian's role in a virtual reality-based pedestrian simulator and experience three encounter scenarios with an automated vehicle.  ...  The efficiency and perceived safety of different signal types on the eHMI are analyzed by collecting data on the subjects' movement behavior and ratings via questionnaires.  ...  These complementary graphs are incorporated into a Graph Convolution Gated Recurrent Unit for spatial-temporal representation learning.  ... 
doi:10.1109/tits.2022.3160062 fatcat:4gklzaonfzcehnvps6oge35fwe

Application and Analysis of Education and Teaching Mode Based on 5G and Smart Technology

Ran Yan, Le Sun
2022 Scientific Programming  
Class performance, based on Yebes network, proposed a learning decision method and application.  ...  improved, indicating that the network multimedia teaching mode can stimulate students' learning interest more, improve learning efficiency. (3) Studying the differences in the source of curriculum resources  ...  Supported by the 5G network environment, teaching evaluation collects a full range of data through the observation of learners' online or offline learning process, discovers their participation in learning  ... 
doi:10.1155/2022/7861157 fatcat:crtjvhjdhje3zp46bksqe3ymha

Relational Graph Learning for Crowd Navigation [article]

Changan Chen, Sha Hu, Payam Nikdel, Greg Mori, Manolis Savva
2020 arXiv   pre-print
Our approach reasons about the relations between all agents based on their latent features and uses a Graph Convolutional Network to encode higher-order interactions in each agent's state representation  ...  We present a relational graph learning approach for robotic crowd navigation using model-based deep reinforcement learning that plans actions by looking into the future.  ...  Recent work [35] proposed to use graph convolutional networks in navigation and used human gaze data to train the network.  ... 
arXiv:1909.13165v3 fatcat:x4dayu4n3rgnnnssik5ey2fk2y

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Time Series Forecasting with Temporal Attention Convolutional Neural Networks Leonardos Pantiskas, Kees Verstoep and Henri Bal .......... 1687 Online System Identification for Nonlinear Uncertain Dynamical  ...  Mormille and Masayasu Atsumi .......... 2670 Evolving Optimal Convolutional Neural Networks Subhashis Banerjee and Sushmita Mitra .......... 2677 GPCNN: Evolving Convolutional Neural Networks using Genetic  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li
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