After two “cold winters of artificial intelligence”, machine learning has once again entered the public’s vision in recent ten years, and has a momentum of rapid development. It has achieved great success in practical applications such as image recognition and speech recognition system. It is one of the main tasks and objectives of machine learning to summarize key information and main features from known data sets, so as to accurately identify and predict new data. From this perspective, the idea of integrating machine learning into climate prediction is feasible. Firstly, taking the adjustment of linear fitting parameters (i.e. slope and intercept) as an example, this paper introduces the process of machine learning optimizing parameters through gradient descent algorithm and finally obtaining linear fitting function. Secondly, this paper introduces the construction idea of neural network and how to apply neural network to fit nonlinear function. Finally, the framework principle of convolutional neural network for deep learning is described, and the convolutional neural network is applied to the return test of monthly temperature in winter in East Asia, and compared with the return results of climate dynamic model. This paper will help to understand the basic principle of machine learning and provide some reference ideas for the application of machine learning to climate prediction.
{"title":"Principle of Machine Learning and Its Potential Application in Cli-mate Prediction","authors":"Shengping He, Huijun Wang, Hua Li, Jiazhen Zhao","doi":"10.32629/jai.v4i1.483","DOIUrl":"https://doi.org/10.32629/jai.v4i1.483","url":null,"abstract":"After two “cold winters of artificial intelligence”, machine learning has once again entered the public’s vision in recent ten years, and has a momentum of rapid development. It has achieved great success in practical applications such as image recognition and speech recognition system. It is one of the main tasks and objectives of machine learning to summarize key information and main features from known data sets, so as to accurately identify and predict new data. From this perspective, the idea of integrating machine learning into climate prediction is feasible. Firstly, taking the adjustment of linear fitting parameters (i.e. slope and intercept) as an example, this paper introduces the process of machine learning optimizing parameters through gradient descent algorithm and finally obtaining linear fitting function. Secondly, this paper introduces the construction idea of neural network and how to apply neural network to fit nonlinear function. Finally, the framework principle of convolutional neural network for deep learning is described, and the convolutional neural network is applied to the return test of monthly temperature in winter in East Asia, and compared with the return results of climate dynamic model. This paper will help to understand the basic principle of machine learning and provide some reference ideas for the application of machine learning to climate prediction.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44704163","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}
In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.
{"title":"Automatic Text Summarization for Urdu Roman Language by Using Fuzzy Logic","authors":"Zeshan Ali","doi":"10.32629/jai.v3i2.273","DOIUrl":"https://doi.org/10.32629/jai.v3i2.273","url":null,"abstract":"In the new era of technology, there is the redundancy of information in the internet world, which gives a hard time for users to contain the willed outcome it, to crack this hardship we need an automated process that riddle and search the obtained facts. Text summarization is one of the normal methods to solve problems. The target of the single document epitome is to raise the possibilities of data. we have worked mostly on extractive stationed text summarization. Sentence scoring is the method usually used for extractive text summarization. In this paper, we built an Urdu Roman Language Dataset which has thirty thousand articles. We follow the Fuzzy good judgment technique to clear up the hassle of text summarization. The fuzzy logic approach model delivers Fuzzy rules which have uncertain property weight and produce an acceptable outline. Our approach is to use Cosine similarity with Fuzzy logic to suppress the extra data from the summary to boost the proposed work. We used the standard Testing Method for Fuzzy Logic Urdu Roman Text Summarization and then compared our Machine-generated summary with the help of ROUGE and BLEU Score Method. The result shows that the Fuzzy Logic approach is better than the preceding avenue by a meaningful edge.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"69960400","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}
Fractional PID controller is a convenient fractional structure that has been used to solve many problems in automatic control. The fractional scale proportional-integral-differential controller is a generalization of the integer order PID controller in the complex domain. By introducing two adjustable parameters and , the controller parameter tuning range becomes larger, but the parameter design becomes more complex. This paper presents a new method for the design of fractional PID controllers. Specifically, the parameters of a fractional PID controller are optimized by a particle swarm optimization algorithm. Our simulation results on cold rolling APC system show that the designed controller can achieve control accuracy higher than that of a traditional PID controller.
{"title":"A Fractional PID Controller Based on Particle Swarm Optimization Algorithm","authors":"Yinglei Song","doi":"10.32629/jai.v3i1.94","DOIUrl":"https://doi.org/10.32629/jai.v3i1.94","url":null,"abstract":"Fractional PID controller is a convenient fractional structure that has been used to solve many problems in automatic control. The fractional scale proportional-integral-differential controller is a generalization of the integer order PID controller in the complex domain. By introducing two adjustable parameters and , the controller parameter tuning range becomes larger, but the parameter design becomes more complex. This paper presents a new method for the design of fractional PID controllers. Specifically, the parameters of a fractional PID controller are optimized by a particle swarm optimization algorithm. Our simulation results on cold rolling APC system show that the designed controller can achieve control accuracy higher than that of a traditional PID controller.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47976376","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}
Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.
{"title":"A Study of Neural Machine Translation from Chinese to Urdu","authors":"Zeeshan Khan","doi":"10.32629/jai.v2i4.82","DOIUrl":"https://doi.org/10.32629/jai.v2i4.82","url":null,"abstract":"Machine Translation (MT) is used for giving a translation from a source language to a target language. Machine translation simply translates text or speech from one language to another language, but this process is not sufficient to give the perfect translation of a text due to the requirement of identification of whole expressions and their direct counterparts. Neural Machine Translation (NMT) is one of the most standard machine translation methods, which has made great progress in the recent years especially in non-universal languages. However, local language translation software for other foreign languages is limited and needs improving. In this paper, the Chinese language is translated to the Urdu language with the help of Open Neural Machine Translation (OpenNMT) in Deep Learning. Firstly, a Chineseto Urdu language sentences datasets were established and supported with Seven million sentences. After that, these datasets were trained by using the Open Neural Machine Translation (OpenNMT) method. At the final stage, the translation was compared to the desired translation with the help of the Bleu Score Method.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46061079","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}
S. H. M. S. Andrade, J. M. Alves, Johan S. L. Barbosa, Rafaela R. Souza
The residential electricity consumption tends to expand further and, consequently, stimulates the development of technological tools that allow to establish greater control of energy consumption. Embedded technology systems play an important role in the efficiency of a smart home by providing to users ways to optimize environment management. The implementation of technologies in the residential environment offer to residents a better quality of life and reduce expenses. Therefore, this paper proposes the development of smart electrical outlets able to identify the apparatus connected to them and make available to the user the detailed consumption of each device that was used through a database.
{"title":"Smart Outlet: Smart Electrical Outlet With Device Identification Using NFC","authors":"S. H. M. S. Andrade, J. M. Alves, Johan S. L. Barbosa, Rafaela R. Souza","doi":"10.32629/jai.v2i4.81","DOIUrl":"https://doi.org/10.32629/jai.v2i4.81","url":null,"abstract":"The residential electricity consumption tends to expand further and, consequently, stimulates the development of technological tools that allow to establish greater control of energy consumption. Embedded technology systems play an important role in the efficiency of a smart home by providing to users ways to optimize environment management. The implementation of technologies in the residential environment offer to residents a better quality of life and reduce expenses. Therefore, this paper proposes the development of smart electrical outlets able to identify the apparatus connected to them and make available to the user the detailed consumption of each device that was used through a database.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41384233","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}
In a digital era, traditional areas like Human Resources have to adapt themselves to stay alive and competitive. The processes have been drasticallychanging from paper and talks into systems and workflows. Data is now morethan ever in the spotlight and have become an essential asset to ensure delivery, performance, quality and predictability. But first, data has to be organized, combined, verified, treated and transformed to become meaningful information, not forgetting automatized to be delivered in time and supporting decision making in a daily basis. Business Intelligence (BI) is the tool capable to do it and we are the minds to pull it off.
{"title":"Data Analytics to Increase Performance in the Human Resources Area","authors":"S. H. M. S. Andrade","doi":"10.32629/jai.v2i4.80","DOIUrl":"https://doi.org/10.32629/jai.v2i4.80","url":null,"abstract":"In a digital era, traditional areas like Human Resources have to adapt themselves to stay alive and competitive. The processes have been drasticallychanging from paper and talks into systems and workflows. Data is now morethan ever in the spotlight and have become an essential asset to ensure delivery, performance, quality and predictability. But first, data has to be organized, combined, verified, treated and transformed to become meaningful information, not forgetting automatized to be delivered in time and supporting decision making in a daily basis. Business Intelligence (BI) is the tool capable to do it and we are the minds to pull it off.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42657679","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}
Zhuozheng Wang, Zhuo Ma, Du Xiuwen, Yingjie Dong, Wei Liu
Brain-computer interface (BCI) is an emerging area of research that establishes a connection between the brain and external devices in a completely new way. It provides a new idea about the rehabilitation of brain diseases, human-computer interaction and augmented reality. One of the main problems of implementing BCI is to recognize and classify the motor imagery Electroencephalography(EEG) signals effectively. This paper takes the motor imagery feature data of EEG as the research object to conduct the research of multi-classification method. In this study, we use the Emotiv helmet with 16 biomedical sensors to obtain EEG signal, adopt the fast independent component analysis and the fast Fourier transform to realize signal preprocessing and select the common spatial pattern algorithm to extract the features of the motor imagery EEG signal. In order to improve the accuracy of recognition of EEG signal, a new deep learning network is designed for multi-channel self-acquired EEG data set which is named as min-VGG-LSTMnet in this paper. The network combines Long Short-Term Memory Network with convolutional neural network VGG and achieves the four-class task of the left-hand, right-hand, left-foot and right-foot lifting movements based on motor imagery. The results show that the accuracy of the proposed classification method is at least 8.18% higher than other mainstream deep learning methods.
{"title":"Research on the Key Technologies of Motor Imagery EEG Signal Based on Deep Learning","authors":"Zhuozheng Wang, Zhuo Ma, Du Xiuwen, Yingjie Dong, Wei Liu","doi":"10.32629/jai.v2i4.60","DOIUrl":"https://doi.org/10.32629/jai.v2i4.60","url":null,"abstract":"Brain-computer interface (BCI) is an emerging area of research that establishes a connection between the brain and external devices in a completely new way. It provides a new idea about the rehabilitation of brain diseases, human-computer interaction and augmented reality. One of the main problems of implementing BCI is to recognize and classify the motor imagery Electroencephalography(EEG) signals effectively. This paper takes the motor imagery feature data of EEG as the research object to conduct the research of multi-classification method. In this study, we use the Emotiv helmet with 16 biomedical sensors to obtain EEG signal, adopt the fast independent component analysis and the fast Fourier transform to realize signal preprocessing and select the common spatial pattern algorithm to extract the features of the motor imagery EEG signal. In order to improve the accuracy of recognition of EEG signal, a new deep learning network is designed for multi-channel self-acquired EEG data set which is named as min-VGG-LSTMnet in this paper. The network combines Long Short-Term Memory Network with convolutional neural network VGG and achieves the four-class task of the left-hand, right-hand, left-foot and right-foot lifting movements based on motor imagery. The results show that the accuracy of the proposed classification method is at least 8.18% higher than other mainstream deep learning methods.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2020-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48127880","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}
In recent years, several studies using smart methods and soft computing in the field of HVAC systems has been provided. In this paper, we propose a framework which will strengthen the benefits of the fuzzy logic and neural fuzzy systems to estimate outdoor temperature. In this regard, ANFIS is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The neural fuzzy system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.
{"title":"Outdoor temperature estimation using ANFIS for soft sensors","authors":"Zahra Pezeshki, S. M. Mazinani, E. Omidvar","doi":"10.32629/jai.v2i3.58","DOIUrl":"https://doi.org/10.32629/jai.v2i3.58","url":null,"abstract":"In recent years, several studies using smart methods and soft computing in the field of HVAC systems has been provided. In this paper, we propose a framework which will strengthen the benefits of the fuzzy logic and neural fuzzy systems to estimate outdoor temperature. In this regard, ANFIS is used in effective combination of strategic information for estimating the outdoor temperature of the building. A novel versatile calculation focused around ANFIS is proposed to adjust logical progressions and to weaken the questionable aggravation of estimation information from multisensory. Due to ANFIS accuracy in specialized predictions, it is an effective device to manage vulnerabilities of each experiential framework. The neural fuzzy system can concentrate on measurable properties of the samples throughout the preparation sessions. Reproduction results demonstrate that the calculation can successfully alter the framework to adjust context oriented progressions and has solid combination capacity in opposing questionable data. This sagacious estimator is actualized utilizing Matlab and the exhibitions are explored. The aim of this study is to improve the overall performance of HVAC systems in terms of energy efficiency and thermal comfort in the building.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43882392","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}
Zheng Kun, Mengfei Wei, Li Shenhui, Dong Yang, Xudong Liu
Pedestrian detection is a critical challenge in the field of general object detection, the performance of object detection has advanced with the development of deep learning. However, considerable improvement is still required for pedestrian detection, considering the differences in pedestrian wears, action, and posture. In the driver assistance system, it is necessary to further improve the intelligent pedestrian detection ability. We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection. Firstly, we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics. Secondly, we propose a novel network architecture, namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector. Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent. At last, we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle. The results establish the validity of the approach.
{"title":"Pedestrian Detection in Driver Assistance Using SSD and PS-GAN","authors":"Zheng Kun, Mengfei Wei, Li Shenhui, Dong Yang, Xudong Liu","doi":"10.32629/jai.v2i3.57","DOIUrl":"https://doi.org/10.32629/jai.v2i3.57","url":null,"abstract":"Pedestrian detection is a critical challenge in the field of general object detection, the performance of object detection has advanced with the development of deep learning. However, considerable improvement is still required for pedestrian detection, considering the differences in pedestrian wears, action, and posture. In the driver assistance system, it is necessary to further improve the intelligent pedestrian detection ability. We present a method based on the combination of SSD and GAN to improve the performance of pedestrian detection. Firstly, we assess the impact of different kinds of methods which can detect pedestrians based on SSD and optimize the detection for pedestrian characteristics. Secondly, we propose a novel network architecture, namely data synthesis PS-GAN to generate diverse pedestrian data for verifying the effectiveness of massive training data to SSD detector. Experimental results show that the proposed manners can improve the performance of pedestrian detection to some extent. At last, we use the pedestrian detector to simulate a specific application of motor vehicle assisted driving which would make the detector focus on specific pedestrians according to the velocity of the vehicle. The results establish the validity of the approach.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41477706","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}
K. Lannelongue, M. Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt
In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.
{"title":"Compositional Grounded Language for Agent Communication in Reinforcement Learning Environment","authors":"K. Lannelongue, M. Milly, R. Marcucci, S. Selevarangame, A. Supizet, A. Grincourt","doi":"10.32629/jai.v2i3.56","DOIUrl":"https://doi.org/10.32629/jai.v2i3.56","url":null,"abstract":"In a context of constant evolution of technologies for scientific, economic and social purposes, Artificial Intelligence (AI) and Internet of Things (IoT) have seen significant progress over the past few years. As much as Human-Machine interactions are needed and tasks automation is undeniable, it is important that electronic devices (computers, cars, sensors…) could also communicate with humans just as well as they communicate together. The emergence of automated training and neural networks marked the beginning of a new conversational capability for the machines, illustrated with chat-bots. Nonetheless, using this technology is not sufficient, as they often give inappropriate or unrelated answers, usually when the subject changes. To improve this technology, the problem of defining a communication language constructed from scratch is addressed, in the intention to give machines the possibility to create a new and adapted exchange channel between them. Equipping each machine with a sound emitting system which accompany each individual or collective goal accomplishment, the convergence toward a common ‘’language’’ is analyzed, exactly as it is supposed to have happened for humans in the past. By constraining the language to satisfy the two main human language properties of being ground-based and of compositionality, rapidly converging evolution of syntactic communication is obtained, opening the way of a meaningful language between machines.","PeriodicalId":70721,"journal":{"name":"自主智能(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41951841","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}
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