
浏览全部资源
扫码关注微信
1. 中国科学院计算技术研究所,北京 100190
2. 中国科学院大学,北京 100049
[ "程学旗(1971− ),男,中国科学院计算技术研究所副所长,主要研究方向为大数据分析与挖掘、网络科学与社会计算、信息检索、网络信息安全" ]
[ "徐冰冰(1995− ),女,中国科学院计算技术研究所助理研究员,主要研究方向为网络表示学习、图神经网络等" ]
[ "曹婍(1992− ),女,中国科学院计算技术研究所助理研究员,主要研究方向为可信图数据挖掘、信息传播建模与预测、模型对抗攻击与防御等" ]
[ "刘盛华(1982− ),男,中国科学院计算技术研究所副研究员,主要研究方向为数据科学、人工智能、异常检测、大图挖掘及系统等" ]
[ "陈娟(1991− ),女,中国科学院大学博士生,主要研究方向为知识图谱、图神经网络、开放群智创新等" ]
[ "林磊(1986− ),男,中国科学院计算技术研究所博士后,主要研究方向为创新管理与科技情报、计算机辅助创新等" ]
[ "沈华伟(1982− ),男,中国科学院计算技术研究所研究员,主要研究方向为网络数据挖掘、社交网络分析、图神经网络等" ]
网络出版日期:2022-03,
纸质出版日期:2022-03-15
移动端阅览
程学旗, 徐冰冰, 曹婍, 等. 开放环境下的群智决策:概念、挑战及引领性技术[J]. 智能科学与技术学报, 2022,4(1):45-54.
Xueqi CHENG, Bingbing XU, Qi CAO, et al. Collective decision-making in open environment: concepts, challenges, and leading technologies[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 45-54.
程学旗, 徐冰冰, 曹婍, 等. 开放环境下的群智决策:概念、挑战及引领性技术[J]. 智能科学与技术学报, 2022,4(1):45-54. DOI: 10.11959/j.issn.2096-6652.202219.
Xueqi CHENG, Bingbing XU, Qi CAO, et al. Collective decision-making in open environment: concepts, challenges, and leading technologies[J]. Chinese journal of intelligent science and technology, 2022, 4(1): 45-54. DOI: 10.11959/j.issn.2096-6652.202219.
开放环境下的群智决策是一种群体参与、人机交互、大数据驱动的新型决策模式,致力于借助开放的互联网环境实现复杂问题求解和智能决策。然而,互联网具有高度开放、复杂交互、行为涌现的特性,使得基于互联网的群智决策面临激励机制难设计、决策个体不可控、决策环境多样化、决策信息高度纷杂等挑战。着眼于开放环境下的群智决策,探讨其概念内涵、决策模式及关键挑战,梳理实施群智决策的引领性技术,并针对群智决策的成功案例进行分析,以期支撑新型决策模式在经济、医疗、民生等领域的应用,推动相应领域的进步和变革。
Research on collective decision-making is based on new decision-making theories and their methods are driven by group participation
human-computer interaction
and big data
to realize complex problem solving and intelligent decision-making in an open environment.However
the open environment is of high openness
complex interaction
and emerging behaviors
making collective decision-making face the challenges of difficult incentive mechanism design
uncontrollable decision-making individuals
diverse decision-making environments
and highly complex decosion-making information.Based on the era background of collective decision-making in an open environment
a new decision-making paradigm was proposed
its conceptual connotation
main challenges
and leading technologies for effective implementation of collective decision-making were sorted out.The successful cases of collective decision-making were analyzed
in order to support the application of new decision-making paradigms in the fields of economy
medical care
and people’s livelihood
and to promote progress and changes in the corresponding fields.
HOWE J . The rise of crowdsourcing [J ] . Wired , 2006 , 14 ( 6 ): 176 - 183 .
MALONE T , BERNSTEIN M S . Handbook of collective intelligence [M ] . Cambridge : MIT Press , 2015 .
LI W , WU W J , WANG H M , et al . Crowd intelligence in AI 2.0 era [J ] . Frontiers of Information Technology & Electronic Engineering , 2017 , 18 ( 1 ): 15 - 43 .
SHEN H W , BARABÁSI A L , . Collective credit allocation in science [J ] . PNAS , 2014 , 111 ( 34 ): 12325 - 12330 .
Wikipedia Contributors . Wikipedia [Z ] . 2022 .
SUN X Q , SHEN H W , CHENG X Q . Trading network predicts stock price [J ] . Scientific Reports , 2014 ,4:3711.
LYU S S , OUYANG W T , WANG Y Q , et al . What we vote for? Answer selection from user expertise view in community question answering [C ] // Proceedings of World Wide Web Conference . New York:ACM Press , 2019 : 1198 - 1209 .
KITTUR A , CHI E H , SUH B . Crowdsourcing user studies with Mechanical Turk [C ] // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems .[S.l.:s.n. ] , 2008 : 453 - 456 .
QUINN A J , BEDERSON B B . Human computation:a survey and taxonomy of a growing field [C ] // Proceedings of the SIGCHI Conference on Human Factors in Computing Systems .[S.l.:s.n. ] , 2011 : 1403 - 1412 .
LASECKI W S , SONG Y C , KAUTZ H , et al . Real-time crowd labeling for deployable activity recognition [C ] // Proceedings of the 2013 Conference on Computer Supported Cooperative Work .[S.l.:s.n. ] , 2013 : 1203 - 1212 .
Wikipedia Contributors . Wikipedia:Netflix prize [Z ] . 2022 .
VON AHN L , MAURER B , MCMILLEN C , et al . reCAPTCHA:human-based character recognition via web security measures [J ] . Science , 2008 , 321 ( 5895 ): 1465 - 1468 .
Wikipedia Contributors . Wikipedia:Amazon Mechanical Turk [Z ] . 2022 .
Wikipedia Contributors . Wikipedia:figure eight inc [Z ] . 2022 .
YU H , SHEN Z Q , MIAO C Y , et al . A reputation-aware decision-making approach for improving the efficiency of crowdsourcing systems [C ] // Proceedings of 2013 International Conference on Autonomous Agents and Multi-agent Systems .[S.l.:s.n. ] , 2013 : 1315 - 1316 .
ZHANG Y , VAN DER SCHAAR M . Reputation-based incentive protocols in crowdsourcing applications [C ] // Proceedings of the IEEE INFOCOM . Piscataway:IEEE Press , 2012 : 2140 - 2148 .
SUNAHASE T , BABA Y , KASHIMA H . Pairwise HITS:quality estimation from pairwise comparisons in creator-evaluator crowdsourcing process [C ] // Proceedings of the 31st AAAI Conference on Artificial Intelligence .[S.l.:s.n. ] , 2017 : 977 - 983 .
LYU S S , OUYANG W T , SHEN H W , et al . Finding high-quality unstructured submissions in general crowdsourcing tasks [C ] // Proceedings of the Information Retrieval .[S.l.:s.n. ] , 2018 : 198 - 210 .
JEON J , CROFT W B , LEE J H . Finding semantically similar questions based on their answers [C ] // Proceedings of the 28th Annual international ACM SIGIR Conference on Research and Development in Information Retrieval . New York:ACM Press , 2005 : 617 - 618 .
DUAN H , CAO Y , LIN C Y , et al . Searching questions by identifying question topic and question focus [C ] // Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics:Human Language Tchnologies .[S.l.:s.n. ] , 2008 : 156 - 164 .
CAO X , CONG G , CUI B , et al . The use of categorization information in language models for question retrieval [C ] // Proceedings of the 18th ACM Conference on Information and Knowledge Management . New York:ACM Press , 2009 : 265 - 274 .
CAO X , CONG G , CUI B , et al . A generalized framework of exploring category information for question retrieval in community question answer archives [C ] // Proceedings of the 19th International Conference on World Wide Web . New York:ACM Press , 2010 : 201 - 210 .
JEON J , CROFT W B , LEE J H . Finding similar questions in large question and answer archives [C ] // Proceedings of the 14th ACM International Conference on Information and Knowledge Management . New York:ACM Press , 2005 : 84 - 90 .
ZHAO Z , LU H Q , ZHENG V W , et al . Community-based question answering via asymmetric multi-faceted ranking network learning [C ] // Proceedings of the 31st AAAI Conference on Artificial Intelligence .[S.l.:s.n. ] , 2017 : 3532 - 3538 .
ZHANG X , LI S , SHA L , et al . Attentive interactive neural networks for answer selection in community question answering [C ] // Proceedings of the 31st AAAI Conference on Artificial Intelligence .[S.l.:s.n. ] , 2017 : 3525 - 3531 .
QIU X , HUANG X . Convolutional neural tensor network architecture for community-based question answering [C ] // Proceedings of the 24th International Joint Conference on Artificial Intelligence .[S.l.:s.n. ] , 2015 : 1305 - 1311 .
GJOKA M , KURANT M , BUTTS C T , et al . Walking in Facebook:a case study of unbiased sampling of OSNs [C ] // Proceedings of the IEEE INFOCOM . Piscataway:IEEE Press , 2010 : 1 - 9 .
WANG T Y , CHEN Y , ZHANG Z B , et al . Unbiased sampling in directed social graph [C ] // Proceedings of the ACM SIGCOMM 2010 Conference on SIGCOMM . New York:ACM Press , 2010 : 401 - 402 .
SALEHI M , RABIEE H R , NABAVI N , et al . Characterizing twitter with respondent-driven sampling [C ] // Proceedings of 2011 IEEE 9th International Conference on Dependable,Autonomic and Secure Computing . Piscataway:IEEE Press , 2011 : 1211 - 1217 .
PARISI G I , KEMKER R , PART J L , et al . Continual lifelong learning with neural networks:a review [J ] . Neural Networks , 2019 , 113 : 54 - 71 .
LI Z Z , HOIEM D . Learning without forgetting [J ] . IEEE Transactions on Pattern Analysis and Machine Intelligence , 2018 , 40 ( 12 ): 2935 - 2947 .
REBUFFI S A , KOLESNIKOV A , SPERL G , et al . iCaRL:incremental classifier and representation learning [C ] // Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition . Piscataway:IEEE Press , 2017 : 5533 - 5542 .
MIYATO T , DAI A M , GOODFELLOW I . Adversarial training methods for semi-supervised text classification [J ] . arXiv preprint , 2016 ,arXiv:1605.07725.
GOODFELLOW I J , SHLENS J , SZEGEDY C . Explaining and harnessing adversarial examples [J ] . arXiv preprint , 2015 ,arXiv:1412.6572.
XIE C H , WANG J Y , ZHANG Z S , et al . Mitigating adversarial effects through randomization [J ] . arXiv preprint , 2018 ,arXiv:1711.01991.
LIU X Q , CHENG M H , ZHANG H , et al . Towards robust neural networks via random self-ensemble [C ] // Proceedings of 2018 European Conference on Computer Vision .[S.l.:s.n. ] , 2018 : 369 - 385 .
VINCENT P , LAROCHELLE H , BENGIO Y , et al . Extracting and composing robust features with denoising autoencoders [C ] // Proceedings of the 25th International Conference on Machine Learning .[S.l.:s.n. ] , 2008 : 1096 - 1103 .
程学旗 , 梅宏 , 赵伟 , 等 . 数据科学与计算智能:内涵、范式与机遇 [J ] . 中国科学院院刊 , 2020 , 35 ( 12 ): 1470 - 1481 .
CHENG X Q , MEI H , ZHAO W , et al . Data science and computing intelligence:concept,paradigm,and opportunities [J ] . Bulletin of Chinese Academy of Sciences , 2020 , 35 ( 12 ): 1470 - 1481 .
李国杰 , 程学旗 . 大数据研究:未来科技及经济社会发展的重大战略领域:大数据的研究现状与科学思考 [J ] . 中国科学院院刊 , 2012 , 27 ( 6 ): 647 - 657 .
LI G J , CHENG X Q . Research status and scientific thinking of big data [J ] . Bulletin of Chinese Academy of Sciences , 2012 , 27 ( 6 ): 647 - 657 .
0
浏览量
270
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构
京公网安备11010802024621