2019 IEEE International Conference on Agents (ICA)最新文献

Diabetic Retinopathy Classification Using an Efficient Convolutional Neural Network 基于高效卷积神经网络的糖尿病视网膜病变分类

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929191

Jiaxi Gao, Cyril Leung, C. Miao

引用次数: 14

ICA 2019 Conference Organization ICA 2019会议组织

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/agents.2019.8929193

引用次数: 0

Forecasting Interaction of Exchange Rates Between Fiat Currencies and Cryptocurrencies Based on Deep Relation Networks 基于深度关系网络的法币与加密货币汇率交互预测

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929155

Chiao-Ting Chen, Lin-Kuan Chiang, Y. Huang, Szu-Hao Huang

{"title":"Forecasting Interaction of Exchange Rates Between Fiat Currencies and Cryptocurrencies Based on Deep Relation Networks","authors":"Chiao-Ting Chen, Lin-Kuan Chiang, Y. Huang, Szu-Hao Huang","doi":"10.1109/AGENTS.2019.8929155","DOIUrl":"https://doi.org/10.1109/AGENTS.2019.8929155","url":null,"abstract":"Forecasting exchange rates is difficult because financial time-series data is too complicated to analyze. In traditional financial studies, economic models and statistic approaches were widely used for predicting exchange rates. Recently, machine learning and deep learning techniques have played increasingly important roles in financial technology studies. This study adopts a deep learning technique called relation networks (RNs) to predict the exchange rates of fiat currencies and cryptocurrencies. To discover the relationship among different currencies, the concept of visual question answering (VQA) is applied in RNs. We also propose a specially designed architecture for the feature extraction stage to consider both spatial and temporal relationships simultaneously. The experimental results show that the proposed approach can achieve higher prediction performance for cryptocurrencies with approximately 65% accuracy rate. We aim to improve traditional approaches and construct a model using the concept of VQA based on RNs to optimize the prediction performance between fiat currencies and cryptocurrencies.","PeriodicalId":235878,"journal":{"name":"2019 IEEE International Conference on Agents (ICA)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128096266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Robo-Advisor Design using Multiobjective RankNets with Gated Neural Network Structure 基于门控神经网络结构的多目标rank网络机器人顾问设计

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929188

Pei-Ying Wang, Chun-Shou Liu, Yao-Chun Yang, Szu-Hao Huang

引用次数: 4

Dynamic pricing method to maximize utilization of one-way car sharing service 利用动态定价方法最大化利用单向拼车服务

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929128

Toya Kamatani, Yusuke Nakata, S. Arai

引用次数: 10

Comfortable Driving by using Deep Inverse Reinforcement Learning 使用深度逆强化学习的舒适驾驶

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929214

Daiko Kishikawa, S. Arai

{"title":"Comfortable Driving by using Deep Inverse Reinforcement Learning","authors":"Daiko Kishikawa, S. Arai","doi":"10.1109/AGENTS.2019.8929214","DOIUrl":"https://doi.org/10.1109/AGENTS.2019.8929214","url":null,"abstract":"Passenger comfort and their safety are pre-requisites to realizing autonomous driving vehicles. Herein, we define “comfortable driving” by considering “comfortability”, with which less physical and mental burden for passengers. Deep reinforcement learning, which has several applications in the autonomous driving domain, is an effective approach to achieve the comfortable driving. Generally, reward function in deep reinforcement learning is expressed quantitatively. However, because obtaining a quantitative expression for comfortable driving is difficult, there is no guarantee that a reward function can satisfy “comfortable driving” conditions. Therefore, we propose an approach to identify reward function that can realize comfortable driving, using LogReg-IRL, a deep inverse reinforcement learning method in linearly solvable Markov decision process. With the constraint that the maximum lateral acceleration does not exceed a certain threshold value, we could experimentally achieve “comfortable driving”. Additionally, by calculating the gradient for the state input of the state-dependent reward function, we could analyze important states.","PeriodicalId":235878,"journal":{"name":"2019 IEEE International Conference on Agents (ICA)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131736437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A Synonym Extraction Method Based on Intimacy* 一种基于亲密度的同义词提取方法*

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929199

Ru Wang, W. Pan, Jinghui Ma, H. Wang

引用次数: 1

Cooperative Behavior by Multi-agent Reinforcement Learning with Abstractive Communication 基于抽象通信的多智能体强化学习的合作行为

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929151

Jin Tanda, Ahmed Moustafa, Takayuki Ito

{"title":"Cooperative Behavior by Multi-agent Reinforcement Learning with Abstractive Communication","authors":"Jin Tanda, Ahmed Moustafa, Takayuki Ito","doi":"10.1109/AGENTS.2019.8929151","DOIUrl":"https://doi.org/10.1109/AGENTS.2019.8929151","url":null,"abstract":"Reinforcement learning (RL) is a major area of machine learning that aims to develop intelligent agents that are able to adapt in random environments appropriately. In this regard, RL has shown good results when applied to complex tasks such as playing video games. In addition, in multi-agent environments, RL has shown strong potential especially with the recent developments. However, there exist few studies that focus on developing cooperation among learning agents. In general, cooperative behavior among learning agents shows higher performance than independent agent behavior. Therefore, in this research, we focus on the cooperative behavior on Predator-Prey game in a continuous space, which is widely used as one of the typical simulation of Multi-agent environment. Especially we focus on predators that their goal is to catch a prey. We propose Leader-Follower model as the organization of predators, and investigate how they cooperate with each other to achieve their goal considering the prey’s policy using a model of RL. The results of our work indicate that a communication between Leader and Followers affects high performance. In addition, we acquire an interesting result as a process of achieving their goal. We investigate the movement locus of them in three cases which is different reward settings, and in each case, they take different policy depending on the reward. We visualize the movement of locus, and discuss about their cooperation and effectiveness.","PeriodicalId":235878,"journal":{"name":"2019 IEEE International Conference on Agents (ICA)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121092126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ICA 2019 TOC

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/agents.2019.8929134

引用次数: 0

Goal-Oriented Modelling for Virtual Assistants 面向目标的虚拟助手建模

2019 IEEE International Conference on Agents (ICA) Pub Date : 2019-10-01 DOI: 10.1109/AGENTS.2019.8929177

Jonathan Leung, Zhiqi Shen, C. Miao

引用次数: 1