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社会计算中组织行为的计算建模与预测方法
Alternative TitleComputational Modeling and Forecasting of Group Behavior in Social Computing
李晓晨
Subtype工学博士
Thesis Advisor曾大军 ; 毛文吉
2012-05-26
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword组织行为预测 社会文化建模 规划知识抽取 规划识别 斯坦纳树算法 Group Behavior Forecasting Social And Cultural Modeling Plan Knowledge Extraction Plan Recognition Steiner Tree Algorithm
Abstract信息技术深刻影响了社会个人、组织的交流方式和相互关系,对人类社会不同文化群体和社会结构产生巨大冲击,造成了社会复杂程度的迅速提高和新兴社会现象及问题的涌现。传统的研究方法已很难对复杂社会系统进行有效分析。在此背景下,利用计算技术研究社会问题的社会计算学科引起了国内外学术界的高度重视。而组织作为社会的主体,其行为是社会计算的关键研究对象。 对组织行为进行计算建模和预测能帮助发现和认识组织行为规律,从而有效分析组织行为和辅助决策制定。然而,组织行为的计算研究难度极大,部分原因在于组织行为复杂性较高。此外,组织历史数据匮乏是另一个主要的原因,这也导致了计算建模以及实验验证难以展开,极大的阻碍了组织行为的计算研究。 为应对以上挑战,本文从社会计算角度出发,研究了两种组织行为建模和预测方法,即数据驱动的建模与预测方法和知识驱动的建模与预测方法。其主要贡献归纳如下: 1)研究了数据驱动的组织行为建模与预测方法。我们对该方向的代表性研究方法—社会文化建模进行研究。基于标准组织行为数据集,采用准确率、召回率、AUC性能指标,全面实验比较和评估社会文化建模方法与其他六种经典预测方法的性能,发现这些算法的准确率较高而召回率则较低,指出了数据驱动的组织行为建模与预测中存在的类不均衡问题,并探讨了可能的应对方法,最后分析了这类方法的优缺点。 2)针对组织行为数据集匮乏的困境,我们提出了一种从开源新闻文本中自动提取组织行为知识并构建组织行为规划的方法。首先定义了组织行为的自然语言形式,设计信息抽取方法从文本中抽取出组织行为及其所对应的前提及结果,并对抽取的行为和状态进行知识求精,同时建立了从常识知识库到组织行为知识的映射以弥补部分缺失的组织行为知识,最后改进智能规划算法以将获取的行为知识自动连接成行为规划。此外,还设计了信息抽取模板从文本中直接抽取行为对,与行为规划结合以提高其质量。该方法首创了从开源数据中自动构建行为规划的计算流程,为应对组织历史数据缺乏的问题提供了崭新的思路和可行的手段。 3)基于构建的组织行为规划,我们研究了实际组织行为的特点,发现了组织行为的多规划特性和非完全可观测性,并设计一种多规划推理方法。该方法将多规划识别问题映射到经典图论问题,进而利用图论算法对问题进行求解。该方法主要面向非完全可观测环境预测组织当前所执行的一个或多个规划,算法复杂度为多项式复杂度,适合于复杂组织行为的预测。此外,针对该方法在实际应用中可能存在的在线识别、谓词逻辑表示、不确定性等问题,进行了方法的功能扩展,完善了该行为预测方法的理论和方法基础。
Other AbstractInformation technology has fundamentally changed different cultural groups and social structure of modern society, influenced interaction patterns and relations between individuals and organizations, and increased the complexity and interaction intensity of society. A lot of new social phenomenon and social problems have emerged quickly and traditional approaches cannot analyze these problems effectively. As a new research field of using computational techniques to tackle social problems, social computing has increasingly gained attention in recent years. Groups are the main constituent of the society and their behavior are the main focus of social computing research. Modeling and forecasting group behavior with computational approaches can help discover and understand the underlying mechanism. However, it is rather challenging to analyze group behavior. It is partly due to the high complexity of group behavior. Another main reason is the lack of historical data. This poses great difficulties for computational modeling and experimental validation and thus hinders the computational studies of group behavior. To address these challenges, we investigate two types of modeling and forecasting approaches from a social computing perspective, i.e., data-driven modeling and forecasting and knowledge-driven modeling and forecasting. The contributions are listed as follows. First, we investigate data-driven modeling and forecasting approaches for group behavior. We choose social and cultural modeling as a representive approach. Based on the benchmark datasets of group behavior, we conduct experiments to compare and evaluate the performance of social and cultural modeling and other six classical forecasting approaches with accuracy, recall, AUC measures. We discover that all these algorithms achieve high accuracy and low recall. We point out the class imbalance problem in data-driven modeling and forecasting and propose possible solutions. We further discuss the strenghs and weaknesses of data-driven modeling and forecasting approaches. Second, to acquire group behavior datasets, we propose an approach to extract the knowledge of group actions from online news and construct the plans of groups automatically. We first define the standard form of group actions and states and design an information extraction approach to extract group actions and associated states (i.e., preconditions and effects). To complement the missing action knowledge, we further refine the extr...
shelfnumXWLW1763
Other Identifier200918014629089
Language中文
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/6421
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
李晓晨. 社会计算中组织行为的计算建模与预测方法[D]. 中国科学院自动化研究所. 中国科学院研究生院,2012.
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