Please wait a minute...
Advanced Search
现代图书情报技术  2016, Vol. 32 Issue (7-8): 32-41     https://doi.org/10.11925/infotech.1003-3513.2016.07.05
  本期目录 | 过刊浏览 | 高级检索 |
基于BDI-Agent模型的突发事件网络舆情应急响应建模研究*
吴鹏1,2,3(),金贝贝1,2,3,强韶华4
1南京理工大学经济管理学院 南京 210094
2江苏省社会公共安全科技协同创新中心 南京 210094
3安全预警与应急联动技术湖北省协同创新中心 武汉 430070
4南京工业大学经济管理学院 南京 211800
A BDI-Agent Based Model for Public Opinion Crisis Response
Wu Peng1,2,3(),Jin Beibei1,2,3,Qiang Shaohua4
1School of Economics and Management, Nanjing University of Science &Technology, Nanjing 210094, China
2Jiangsu Collaborative Innovation Center of Social Safety Science and Technology, Nanjing 210094, China
3Hubei Collaborative Innovation Center for Early Warning and Emergency Response Technology, Wuhan 430070, China
4School of Economics and Management, Nanjing University of Technology, Nanjing 211800, China
全文: PDF (556 KB)   HTML ( 60
输出: BibTeX | EndNote (RIS)      
摘要 

目的】基于“信念-愿望-意图”(BDI)模型分析网民在突发事件网络舆情中行为决策的动因和发展规律, 引导网民情绪, 建立复杂网络情境下可推理、可预测的应急响应计算模型。【方法】基于“信念-愿望-意图”模型建立起网络舆情演变过程中各类主体(网民、政府、媒体)的多Agent交互模型, 对网民的心智状态的转换过程建模仿真, 从而揭示网络舆情演变的内在动因, 支持应急响应策略的科学制定。本文以突发事件网络舆情中网民情感倾向性为核心, 面向网民、政府、媒体的交互, 设计BDI-Agent概念模型, 包括Agent语境和推理规则设计; 在此基础上设计实证模型, 包括Agent属性、推理规则和交互规则设计, 并结合实际案例进行验证。【结果】结合典型突发事件网络舆情案例进行实证研究, 验证本文提出的多Agent模型的科学性。【局限】该模型需要更多同类事件的对比和优化。【结论】可以基于BDI模型将复杂的网络舆情映射为一个可以规约推理的计算模型, 并为突发事件网络舆情演变趋势的预测和应急策略的制定提供一套可参考的实证模型。

服务
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章
吴鹏
金贝贝
强韶华
关键词 突发事件网络舆情应急响应“信念-愿望-意图”模型    
Abstract

[Objective] This paper analyzes the motivation and evolution of netizens’ behavior in crises with the help of the “Belief-Desire-Intention (BDI)” model to guide netizens’ emotion, and then builds a computing model for crisis response in the complex network environment. [Methods] First, we designed a model for interaction among netizens, government and media in public opinion crisis to simulate the netizens’ emotional changes, based on the BDI-Agent theoretical model. This model could reveal reasons for the changing of public opinion and help us create better crisis response strategies. Second, we built an experimental model with the Agent property, reasoning rules and interaction designs to examine the algorithm with real world cases. [Results] Our empirical study showed that the proposed model was feasible. [Limitations] More real world cases were needed to further optimize the new model. [Conclusions] The proposed BDI-agent model could map the complicated public opinion context to a reasonable computing model, which could help us predict the future development of the public opinion crises and design better response strategy.

Key wordsEmergency affairs    Public opinion    Crisis response    Belief-Desire-Intention model
收稿日期: 2016-06-12      出版日期: 2016-09-29
基金资助:*本文系国家自然科学基金面上项目“突发事件网络舆情演变过程中的人群仿真研究”(项目编号:71273132)、国家自然科学基金面上项目“社会化影响下个体信息认知处理中的扭曲与偏见机制研究”(项目编号:71471089)和国家哲学社会科学重点基金项目“大数据环境下社会舆情与决策支持方法体系研究”(项目编号:14AZD084)的研究成果之一
引用本文:   
吴鹏,金贝贝,强韶华. 基于BDI-Agent模型的突发事件网络舆情应急响应建模研究*[J]. 现代图书情报技术, 2016, 32(7-8): 32-41.
Wu Peng,Jin Beibei,Qiang Shaohua. A BDI-Agent Based Model for Public Opinion Crisis Response. New Technology of Library and Information Service, 2016, 32(7-8): 32-41.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.1003-3513.2016.07.05      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2016/V32/I7-8/32
[1] Stephens K K, Malone P C.If the Organizations won’t Give Us Information: The Use of Multiple New Media for Crisis Technical Translation and Dialogue[J]. Journal of Public Relations Research, 2009, 21(2): 229-239.
[2] Jin Y, Liu B F.The Blog-mediated Crisis Communication Model: Recommendations for Responding to Influential External Blogs[J]. Journal of Public Relations Research, 2010, 22(4): 429-455.
[3] Liu B F, Jin Y, Briones R, et al.Managing Turbulence in the Blogosphere: Evaluating the Blog-mediated Crisis Communication Model with the American Red Cross[J]. Journal of Public Relations Research, 2012, 24(4): 353-370.
[4] 王昌伟. 网络危机信息传播机理与仿真研究[D]. 哈尔滨: 哈尔滨工程大学, 2012.
[4] (Wang Changwei.Network Crisis Information Propagation Mechanisms and Simulation[D]. Harbin: Harbin Engineering University, 2012.)
[5] 岳峰, 胡晓峰, 李志强, 等. 多智能体涌现生成的群体行为仿真[J]. 系统仿真学报, 2008, 20(S): 205-208.
[5] (Yue Feng, Hu Xiaofeng, Li Zhiqiang, et al.Generating Crowd Behavior Simulation from Multi-agent’s Emergence[J]. Journal of System Simulation, 2008, 20(S): 205-208.)
[6] DeAnaelis D L, Mooij W M. Individual-based Modeling of Ecological and Evolutionary Processes[J]. Annual Review of Ecology Evolution & Systematics, 2005, 36: 147-168.
[7] Johnson W L, Beal C, Fowles-Winkler A, et al.Tactical Language Training System: An Interim Report [C]. In: Proceedings of the International Conference on Intelligent Tutoring Systems. Springer Berlin Heidelberg.2004: 336-345.
[8] Oser R L.A Structured Approach for Scenario-based Training [C]. In: Proceedings of the 43rd Annual Meeting of the Human Factors and Ergonomics Society, Houston, TX, USA.1999: 1138-1142.
[9] Van Den Bosch K, Riemersma J B J. Reflections on Scenario- based Training in Tactical Command [A] . // Schiflett S, Elliot L, Salas E, et al. Scaled Worlds: Development, Validation and Applications [M]. Burlington, VT: Ashgate Publishing, 2004: 1-21.
[10] Cohen P R, Levesque H J.Intention is Choice with Commitment[J]. Artificial Intelligence, 1990, 42(2-3): 213-261.
[11] Wooldridge M.Reasoning about Rational Agents [M]. Cambridge, MA: The MIT Press, 2003.
[12] Rao A S, Georgeff M P.Decision Procedures for BDI Logics[J]. Journal of Logic and Computation, 1998, 8(3): 293-343.
[13] Norling E, Sonenberg L.Creating Interactive Characters with BDI Agents [C]. In: Proceedings of the Australian Workshop on Interactive Entertainment IE 2004, Sydney, Australia. 2004: 118-209.
[14] Magerko B, Laird J E.Towards Building an Interactive, Scenario-based Training Simulator [C]. In: Proceedings of the Behavior and Representation and Computer Generated Forces Conference.2002: 15-34.
[15] Magerko B, Laird J E, Assanie M, et al.AI Characters and Directors for Interactive Computer Games [C]. In: Proceedings of the 16th Conference on Innovative Applications of Artificial Intelligence. AAAI Press, 2004: 877-883.
[16] Niewiadomski R, Bevacqua E, Mancini M, et al.Greta: An Interactive Expressive ECA System [C]. In: Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems-Volume 2. International Foundation for Autonomous Agents and Multiagent Systems, 2009: 1399-1400.
[17] 刘芳. 基于过程本体的异质Agent协作技术研究[D]. 长沙: 国防科学技术大学, 2006.
[17] (Liu Fang.Research on Process Ontology Based Heterogeneous Agent Cooperation Technology [D]. Changsha: National University of Defense Technology, 2006.)
[18] Casali A, Godo L, Sierra C.A Logical Framework to Represent and Reason about Graded Preferences and Intentions [C]. In: Proceedings of the 11th International Conference on Principles of Knowledge Representation and Reasoning. 2008: 27-37.
[19] Criado N, Argente E, Botti V.Normative Deliberation in Graded BDI Agents [C]. In: Proceedings of the 8th German Conference, MATES 2010, Leipzig, Germany. 2010, 6251: 52-63.
[20] Giunchiglia F, Serafini L.Multilanguage Hierarchical Logics, or: How We can do without Modal Logics[J]. Artificial Intelligence, 1994, 65(1): 29-70.
[21] Meyer J-J C. Dynamic Logic for Reasoning about Actions and Agents [A]. // Logic-based Artificial Intelligence[M]. Springer US, 2000: 281-311.
[22] Casali A.On Intentional and Social Agents with Graded Attitudes [D]. Universitat de Girona, 2008.
[23] Criado N, Argente E, Botti V.Rational Strategies for Norm Compliance in the n-BDI Proposal [C]. In: Proceedings of the COIN 2010 International Workshops, Toronto, Canada. Springer Berlin Heidelberg, 2011: 1-20.
[24] Criado N, Argente E, Noriega P, et al.Human-inspired Model for Norm Compliance Decision Making[J]. Information Sciences, 2013, 245: 218-239.
[25] Das D, Bandyopadhyay S.Extracting Emotion Topics from Blog Sentences: Use of Voting from Multi-engine Supervised Classifiers [C]. In: Proceedings of the 2nd International Workshop on Search and Mining User-generated Contents. ACM, 2010: 119-126.
[26] Paltoglou G, Gobron S, Skowron M, et al.Sentiment Analysis of Informal Textual Communication in Cyberspace [C]. In: Proceedings of Engage 2010. 2010: 13-25.
[27] Fonte F A M, Burguillo J C, Nistal M L. An Intelligent Tutoring Module Controlled by BDI Agents for an e-Learning Platform[J]. Expert Systems with Applications, 2012, 39(8): 7546-7554.
[1] 范涛,王昊,吴鹏. 基于图卷积神经网络和依存句法分析的网民负面情感分析研究*[J]. 数据分析与知识发现, 2021, 5(9): 97-106.
[2] 程铁军, 王曼, 黄宝凤, 冯兰萍. 基于CEEMDAN-BP模型的突发事件网络舆情预测研究*[J]. 数据分析与知识发现, 2021, 5(11): 59-67.
[3] 尹浩然,曹金璇,曹鲁喆,王国栋. 扩充语义维度的BiGRU-AM突发事件要素识别研究*[J]. 数据分析与知识发现, 2020, 4(9): 91-99.
[4] 邓建高,张璇,傅柱,韦庆明. 基于系统动力学的突发事件网络舆情传播研究:以“江苏响水爆炸事故”为例*[J]. 数据分析与知识发现, 2020, 4(2/3): 110-121.
[5] 梁艳平,安璐,刘静. 同类突发公共卫生事件微博话题共振研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 122-133.
[6] 丁晟春,俞沣洋,李真. 网络舆情潜在热点主题识别研究*[J]. 数据分析与知识发现, 2020, 4(2/3): 29-38.
[7] 黄微,赵江元,闫璐. 网络热点事件话题漂移指数构建与实证研究*[J]. 数据分析与知识发现, 2020, 4(11): 92-101.
[8] 梅妍霜,朱恒民,魏静. 媒体协同对网络舆情扩散的作用机制研究*[J]. 数据分析与知识发现, 2019, 3(2): 65-71.
[9] 胡哲,查先进,严亚兰. 突发事件情境下在线健康社区用户交互行为研究 *[J]. 数据分析与知识发现, 2019, 3(12): 10-20.
[10] 李纲,陈思菁,毛进,谷岩松. 自然灾害事件微博热点话题的时空对比分析 *[J]. 数据分析与知识发现, 2019, 3(11): 1-15.
[11] 贾隆嘉, 张邦佐. 高校网络舆情安全中主题分类方法研究*——以新浪微博数据为例[J]. 数据分析与知识发现, 2018, 2(7): 55-62.
[12] 王璟琦, 李锐, 吴华意. 基于空间自相关的网络舆情话题演化时空规律分析*[J]. 数据分析与知识发现, 2018, 2(2): 64-73.
[13] 李真, 丁晟春, 王楠. 网络舆情观点主题识别研究*[J]. 数据分析与知识发现, 2017, 1(8): 18-30.
[14] 王晰巍, 张柳, 李师萌, 王楠阿雪. 新媒体环境下社会公益网络舆情传播研究* ——以新浪微博“画出生命线”话题为例[J]. 数据分析与知识发现, 2017, 1(6): 93-101.
[15] 丁晟春,龚思兰,李红梅. 基于突发主题词和凝聚式层次聚类的微博突发事件检测研究*[J]. 现代图书情报技术, 2016, 32(7-8): 12-20.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
版权所有 © 2015 《数据分析与知识发现》编辑部
地址:北京市海淀区中关村北四环西路33号 邮编:100190
电话/传真:(010)82626611-6626,82624938
E-mail:jishu@mail.las.ac.cn