Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference最新文献

Research on How Social Support Affects the Elderly's Life Satisfaction: Analysis of Intermediate Effect Based on Mental Health and Physical Health 社会支持对老年人生活满意度的影响研究:基于心理健康和生理健康的中介效应分析

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407708

L. Bing, Li Dongmei, Zhao Qin

引用次数: 2

Reinforcement Learning based Strategy Selection in StarCraft: Brood War 《星际争霸:母巢之战》中基于强化学习的策略选择

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407726

Taidong Zhang, Xianze Li, Xudong Li, Guanghui Liu, Miao Tian

{"title":"Reinforcement Learning based Strategy Selection in StarCraft: Brood War","authors":"Taidong Zhang, Xianze Li, Xudong Li, Guanghui Liu, Miao Tian","doi":"10.1145/3407703.3407726","DOIUrl":"https://doi.org/10.1145/3407703.3407726","url":null,"abstract":"Artificial intelligence (AI) applied on real-time strategy (RTS) games, such as StarCraft has become a hot topic in recent years. Currently, many researches mainly focused on the aspects of applying reinforcement learning (RL) on the aspects, such as the building order, map analysis, macro- and micro-management, etc. This paper innovatively proposes a mechanism applying reinforcement learning on pre-designed game strategies, which can be considered as human experience and combinations of both the macro- and micro-management. In our work, game strategies are formed by many sub-strategy modules that consist a series of orderly actions corresponding to certain components and game stages. Further, by applying RL on the strategy modules, the proposed mechanism can effectively improve selection of the game strategies, and balance the AI decision and human experience. Our bot was tested against some bots selected from AIIDE 2017-2018. The results show that with the RL framework, our bot can improve the overall winning rate from 42.79% to 59.36%. Particularly, the improvements on opponent 32.06%, 25.18%, and 30.32%, against the Zerg bot, Arrakhammer, the Protoss bot, Xelnaga, the Terran bot, UAlbertaBot.","PeriodicalId":284603,"journal":{"name":"Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131595466","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

Fast AI classification for analyzing construction accidents claims 快速人工智能分类分析建筑事故索赔

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407705

R. Li, H. Li, Beiqi Tang, Wai-hong Au

{"title":"Fast AI classification for analyzing construction accidents claims","authors":"R. Li, H. Li, Beiqi Tang, Wai-hong Au","doi":"10.1145/3407703.3407705","DOIUrl":"https://doi.org/10.1145/3407703.3407705","url":null,"abstract":"Safety has long been considered an important issue in the construction industry. One means of reducing accidents is to provide for heavy compensation. As per the common law system, precedents, once made, then become part of the legal system. Therefore, construction companies and legal firms have an interest in obtaining details of court cases relevant to the ones they are currently involved with. However, the cost of identifying relevant court cases can be excessive. Computer-based text classification, the process of classifying documents into predefined categories with regard to their content, is proposed in this paper as a way to speed up the procedure whereby court cases are identified as relevant to a particular claim for accident compensation. The data set used for this project consisted of 3000 sentences. The 'training split' was 90% training and 10% for testing. The results show that the precision of the system proposed here for classifying construction accident cases is 95.7% and that the recall is 95.7%. This demonstrates that the fastText-based classification employed can successfully classify papers as relevant to the acceptance or rejection of a compensation case at a fairly high rate of accuracy. This pilot research provides a practical example in order to showcase the possibility of utilising artificial intelligence, without human intervention, for document classification. Such a facility could reduce the time taken to identify relevant past cases, so saving human resources, and improving turn-round times.","PeriodicalId":284603,"journal":{"name":"Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124602032","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: 6

Explore the Improvement of the Management of China's International Film Festivals Based on Artificial Intelligence 探索基于人工智能的中国国际电影节管理改进

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407716

Zhu Simeng, Zeng Yizhou, V. Raj

引用次数: 1

A Discussion of the Application of Artificial Intelligence In the Management of Mass Media Censorship in Mainland China 人工智能在中国大陆大众传媒审查管理中的应用探讨

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407719

Zeng Yizhou, Zhu Simeng, V. Raj

引用次数: 1

SNN-Cliq++: Improved Cell Clustering Method Based on Graph snn - cliq++:改进的基于图的细胞聚类方法

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407731

Lilu Guo, S. Bu, Yongjin Gan, Jianbo Lu, Xiaoshu Zhu

{"title":"SNN-Cliq++: Improved Cell Clustering Method Based on Graph","authors":"Lilu Guo, S. Bu, Yongjin Gan, Jianbo Lu, Xiaoshu Zhu","doi":"10.1145/3407703.3407731","DOIUrl":"https://doi.org/10.1145/3407703.3407731","url":null,"abstract":"Cell typing using sing-cell RNA-seq data is the basis of precision medicine, life development & evolution, and drug research & development, etc. However, those data is characterized by ultrahigh dimensions, small samples, no labeling, and high noise, which bring challenges to traditional clustering methods, e.g. poor cell typing performance, high computational cost, and difficulty in parameter adjustment. SNN-Cliq is an outstanding clustering algorithm for cell typing proposed in 2015, with unique characters of simple and efficient computing process, good scalability and insensitiveness of parameters. Based on the frame of previous works [5], three improvements were proposed in our new method, namely SNN-Cliq++. Firstly, we replaced Euclidean distance with Spearman correlation coefficient to measurement the similarity between each cells pairs. Secondly, we optimize parameter k constrained by min |clusterNum-trueNum|, note that this process does not cost much time. Thirdly, we add negative indicate matric to forbid connection between cells which have top negative Spearman correlation coefficient. In extensive datasets, results reveal new algorithms has remarkable improvement than original, NMI rises 20.5% and ARI rises 28.6% in average.","PeriodicalId":284603,"journal":{"name":"Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116132929","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

Research on Adaptive Front-lighting System Algorithm Based on Machine Vision 基于机器视觉的自适应前照灯系统算法研究

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407709

Zheng Tuo

引用次数: 0

Research on Object Detection of Traffic Scene Based on Deep Learning 基于深度学习的交通场景目标检测研究

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407728

Zhou Yan, Zhou Jun, Gui Wei

引用次数: 1

Research on Obstacle Avoidance and Trajectory Planning Strategy of Automatic Parking System 自动泊车系统避障及轨迹规划策略研究

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407715

Yao Cenglin

引用次数: 0

Correlation analysis of factors affecting bridge health under the background of big data 大数据背景下桥梁健康影响因素的相关性分析

Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference Pub Date : 2020-08-20 DOI: 10.1145/3407703.3407711

Ding Wenxia, Li Heping

{"title":"Correlation analysis of factors affecting bridge health under the background of big data","authors":"Ding Wenxia, Li Heping","doi":"10.1145/3407703.3407711","DOIUrl":"https://doi.org/10.1145/3407703.3407711","url":null,"abstract":"Once the bridge is put into use, in addition to its own material aging, it will also receive damage from human factors such as vehicles, wind, earthquakes, fatigue, and overload. This damage has more or less reduced the service life of the bridge or even destroyed the safety performance of the bridge, causing huge losses to people's lives and property. This requires us to pay attention to the safety, reliability and durability of the bridge at all times during the construction of the bridge and in the later maintenance process. The traditional bridge monitoring work and operation and maintenance have a low degree of automation, bridge condition assessment, and bridge real-time monitoring and comprehensive information management is difficult. This requires real-time monitoring of this information in the context of big data to ensure the healthy use of the bridge. In this paper, a finite element analysis model is established to monitor the sensor network of the bridge and the data detected by the sensor in the context of big data. The optimization analysis results in an optimized layout plan of the identifiable static sensors, taking into account both the economic and structural operating conditions of the bridge.","PeriodicalId":284603,"journal":{"name":"Proceedings of the 2020 Artificial Intelligence and Complex Systems Conference","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134398635","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