面向决策支持的组织多源异性知识的智能集成研究
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摘要
管理决策一直以来都是一个充满挑战的领域,并且是一个知识密集型的活动。高质量的管理决策需要决策者具有丰富的经验并擅于综合相关的信息和知识。尤其是在当前急剧变化的复杂竞争环境中,及时正确的决策依赖于掌握更多有价值的信息和知识。人们常把决策失误归咎于缺少信息,实际上缺乏的是能够洞察全局和预见未来的知识。知识管理已经成为管理决策和决策支持所必须关注的重要课题。21世纪以来,在决策过程中管理者们如何运用数据、信息和知识的支持方式已经发生了巨大的变化。决策者面临获取知识过多导致知识过载,知识难以有效利用的问题。同时,组织知识大多是片段的、零散的、异性的、分布在组织的多个知识源之中,也这为决策支持带来了困难。显然,有效的知识集成是解决这些问题的基础和前提。而知识集成中的关键问题知识价值判断、隐性知识表示与集成至今仍未解决。
基于上述问题,本文主要内容和创新点如下：
1.面向决策支持的智能知识集成的理论框架的建立。本文关于知识集成的研究区别于传统知识管理中以组织行为为主的研究路径,而是基于自然决策理论的视角重点讨论以决策者为中心的知识集成问题。针对这一问题,融合决策科学、认知心理学、人工智能、自然决策理论和多Agent系统建立了实现知识获取智能化、知识表示智能化、知识评价智能化和知识应用智能化的系统性理论框架。为本文的理论分析和模型建立奠定了基础。
2.建立了融合因果关系和信任关系的隐性知识表示方法。通过分析和总结人工智能中对于专家知识表示方法的优缺点,提出了以模糊认知图这一软计算方法为基础的隐性知识表示方法。同时,为进一步全面描述决策者在真实环境中对于知识的理解和应用,提出了知识信度的概念来反映专家对于知识的信任程度对决策者形成的影响。从而使得对于专家经验知识的描述更加全面且能够进行一定的定量分析。而且基于模糊认知图的表示方法使得决策问题的形式化和决策知识的形式化统一起来,为进一步的对面向决策支持的知识评价提供了前提和基础。
3.分析了决策过程中知识价值的特征,构建了面向决策支持的知识价值函数。知识的价值在于减少决策情景的不确定性,基于熵理论提出了包含概念价值、关系价值和信度价值的知识价值函数。通过知识价值函数可以定量分析给定的知识源中的某一知识对于减少决策情景不确定性的贡献程度。支持决策者进行知识的筛选和确定决策的时机,从而减少由于知识负载带来的影响。
4.针对组织知识的多源异性的特征,构建了基于多Agent系统的智能知识集成方法,并建立了RPD与CBR相结合的Agent推理机制。在此基础上,提出了通过Agent的共享心智模型实现经验知识集成和融合的新决策支持模式,从而克服由单一Agent决策所带来的有限意识问题。
5.在证据理论的研究框架下,探讨了共享心智模型的构建过程,给出了不同来源的经验知识的融合方法。
6.在多Agent仿真系统Repast中进行了知识价值模型和多Agent知识集成和共享心智模式的仿真实验,对提出的理论模型进行了验证和分析。设计了在多Agent平台JACK下的智能知识集成系统原型。
本文的创新点可以概括为以下几个方面：
1.面向特征的决策问题形式化方法。通过决策问题形式化方法的比较研究,将决策问题形式化问题转化为决策情景的描述,提出建立在决策情景的识别特征和其内在联系基础上的决策问题形式化方法。
2.基于因果关系和信任关系的模糊认知图隐形知识表示方法。基于自然决策理论展开的对决策者决策行为的分析,引入了影响决策者对知识信任程度的知识源间的信任关系,提出了知识信度的概念来反映专家对于知识的信任程度对决策者形成的影响,使得隐性知识的描述更加客观全面并更够进行定量分析。
3.基于熵的知识价值模型。从决策支持的视角分析知识对于减少决策问题不确定性的贡献,提出了基于熵理论的包含概念价值、关系价值和信度价值的知识价值函数。该研究具有原创性的特点,为知识负载的对策研究提供了有益的指导作用。
4.提出基于RPD-BDI代理的智能知识集成方法。在智能代理的知识结构中引入面向知识集成的个体心智模型和共享心智模型结构,提出了RPD与CBR相结合的Agent推理机制,给出了共享心智模型的构建方法。在此基础上,提出了通过Agent的共享心智模型实现经验知识集成和融合的新决策支持模式,从而克服由单一Agent决策所带来的有限意识问题。
总之,对于结合自然决策理论和多Agent系统的面向决策支持的智能知识集成研究尚未有研究者进行过系统的研究。而本文的研究是探索其中的理论基础、关键问题和技术的一种尝试。同时,本文的研究将为决策支持系统和知识管理的研究作为积极的贡献,为实现两者的全面系统的集成提供有益的指导。
Managerial decision-making, as a knowledge intensive activity, is always challenging. To make high quality decision, decision-makers need to be good at integrating relative information and knowledge and be equipped with a great deal of experience. Especially under rapid change and complex competition envorinment, making decision correctly and timely is depand on having valuable information and knowledge. People often consider lack of information will lead to failure in make good decision. Actually, the real reason is that there is no knowledge which has integred view and is able to focaste future. Knowledge management has become a focused topic in managerial decision-making and decision support research area. There are great changes in support model of how managers utilize organizational data, inforamtion and knowledge during their decision making. Managers have to face to knowledge overload because of huge amout of knowledge, which leads to organizational knowledge's usage is very low. More seriously, most of organizational knowledge are segmented, isloated, heterogeneous, and distributed, and existed in different sources. This fact is also making providing effective dicision suppore more difficult. Obviously, effective knowledge integration is the basis and premise of solving those problems. Unfortunately, judgment of knowledge value, presentation and integration of tacit knowledge, as the key factor of knowledge integration, is still in progress.
According to questions mentioned above, this paper has carried out some related research works:
1. Building the theory framework of intelligent knowledge integration towards decision making. The research approach of this paper is different from traditional research path which focus on orgnizational behavior, is from naturalistic decision making perspective to discuss decision-maker centred knowledge integration. In order to discuss this question, this paper established a systematic theory framework which based on decision science, cognitive psychology, artificial intelligence, naturalistic decision making, and multi-agent system, and aimed to research intelligent knowledge acquisition, intelligent knowledge representation, intelligent knowledge evaluation, and intelligent knowledge apply. The theory framework is the foundation of this paper's theory analysis and model formulation.
2. Propose a knowledge representation method to describe tacit knowledge depands on causuality relationship and turst relationship. By reviewing and analyzing knowledge representation methods in artificial intelligence, this paper proposed a tacit knowledge representation method based on fuzzy cognitive maps which is a kind of soft computing method. Futhermore, this paper introduce a novel concept-belief of knowledge, the concept is reflecting how expert is confident on they suggestion.
3. Discuss the features of knowledge's value, giving the value funtion of knowledge towords decision making. The real knowledge value for decision-maker is whether the knowledge could decrease degree of uncertainty in decision situation. The function will calculate the concept value, causuality value and trust value to measure the contribution which the target knowledge may bring to decision problem. Based on the value of knowledge, decision-makers could filter knowledge and choose a right decision point. The result is lowering influence of knowledge overload.
4. In order to overcome problem of access knowledge from heterogeneous knowledge sources, an intelligent approach of integrating knowledge based on multi-agent systems was introduced. Under the multi-agent system, this paper presented a new decision support scheme which implemented by sharing mind model in an intelligent agent in order to overcome bounded awareness. The agent uses the sharing mind model to integrate experience knowledge in the multi-agent system.
5. The mechnism of form sharing mind model is according to the approach provided by Evidence Theory. The purpose is fusion experence knowledge from other agents (including intelligent agent and human).
6. Carry out simulation experiments to evaluate and analysis the knowledge value model, multi-agent knowledge integration and shared mind model under Repast simulation system. Design a prototype of intelligent knowledge integration systems in multi-agent JACK platform.
The new ideas contrubuted to theory and practice is listed below:
1. Propose a method of decision problem formulazation according to key factors and their relationship of decision problem. The method converts decision prolem into decision situation.
2. Provide causuality-trusty fuzzy cognitive map approach to representation tacit knowledge. The approach introduced influence of expert's confidence into knowledge properties, which would get better understand of tacit knowledge. The approach also make tacit knowledge could be analyzed quantitatively.
3. Building knowledge value function based on Entropy Theory. The function includes three elements:concept value function, causualty value function and trust value function. Apply the function in processes of decision making would provide effective way to decrease knowledge overload.
4. Establish a framework of intelligent knowledge integration based on RPD-BDI agent and multi-agent system techniques and proposal a novel RPD-CBR reasoning method of agent. This paper introduced individual mind model and sharing mind model to construct agent's knowledge structure. Depand on sharing mind model, agents could absorb experience knowledge from other agents, which help a single agent overcome its bounded awareness.
Above all, intelligent knowledge integration is a challenging and worthy of exploring. Few researchers have applied naturalistic decision making theory and multi-agent system technique togther to study this area. This paper has tried to discuss the theory foundation, key issues, and relavant technology. And hope to provide some benefits on integrating decision support systems and knowledge management.
基于上述问题,本文主要内容和创新点如下：
1.面向决策支持的智能知识集成的理论框架的建立。本文关于知识集成的研究区别于传统知识管理中以组织行为为主的研究路径,而是基于自然决策理论的视角重点讨论以决策者为中心的知识集成问题。针对这一问题,融合决策科学、认知心理学、人工智能、自然决策理论和多Agent系统建立了实现知识获取智能化、知识表示智能化、知识评价智能化和知识应用智能化的系统性理论框架。为本文的理论分析和模型建立奠定了基础。
2.建立了融合因果关系和信任关系的隐性知识表示方法。通过分析和总结人工智能中对于专家知识表示方法的优缺点,提出了以模糊认知图这一软计算方法为基础的隐性知识表示方法。同时,为进一步全面描述决策者在真实环境中对于知识的理解和应用,提出了知识信度的概念来反映专家对于知识的信任程度对决策者形成的影响。从而使得对于专家经验知识的描述更加全面且能够进行一定的定量分析。而且基于模糊认知图的表示方法使得决策问题的形式化和决策知识的形式化统一起来,为进一步的对面向决策支持的知识评价提供了前提和基础。
3.分析了决策过程中知识价值的特征,构建了面向决策支持的知识价值函数。知识的价值在于减少决策情景的不确定性,基于熵理论提出了包含概念价值、关系价值和信度价值的知识价值函数。通过知识价值函数可以定量分析给定的知识源中的某一知识对于减少决策情景不确定性的贡献程度。支持决策者进行知识的筛选和确定决策的时机,从而减少由于知识负载带来的影响。
4.针对组织知识的多源异性的特征,构建了基于多Agent系统的智能知识集成方法,并建立了RPD与CBR相结合的Agent推理机制。在此基础上,提出了通过Agent的共享心智模型实现经验知识集成和融合的新决策支持模式,从而克服由单一Agent决策所带来的有限意识问题。
5.在证据理论的研究框架下,探讨了共享心智模型的构建过程,给出了不同来源的经验知识的融合方法。
6.在多Agent仿真系统Repast中进行了知识价值模型和多Agent知识集成和共享心智模式的仿真实验,对提出的理论模型进行了验证和分析。设计了在多Agent平台JACK下的智能知识集成系统原型。
本文的创新点可以概括为以下几个方面：
1.面向特征的决策问题形式化方法。通过决策问题形式化方法的比较研究,将决策问题形式化问题转化为决策情景的描述,提出建立在决策情景的识别特征和其内在联系基础上的决策问题形式化方法。
2.基于因果关系和信任关系的模糊认知图隐形知识表示方法。基于自然决策理论展开的对决策者决策行为的分析,引入了影响决策者对知识信任程度的知识源间的信任关系,提出了知识信度的概念来反映专家对于知识的信任程度对决策者形成的影响,使得隐性知识的描述更加客观全面并更够进行定量分析。
3.基于熵的知识价值模型。从决策支持的视角分析知识对于减少决策问题不确定性的贡献,提出了基于熵理论的包含概念价值、关系价值和信度价值的知识价值函数。该研究具有原创性的特点,为知识负载的对策研究提供了有益的指导作用。
4.提出基于RPD-BDI代理的智能知识集成方法。在智能代理的知识结构中引入面向知识集成的个体心智模型和共享心智模型结构,提出了RPD与CBR相结合的Agent推理机制,给出了共享心智模型的构建方法。在此基础上,提出了通过Agent的共享心智模型实现经验知识集成和融合的新决策支持模式,从而克服由单一Agent决策所带来的有限意识问题。
总之,对于结合自然决策理论和多Agent系统的面向决策支持的智能知识集成研究尚未有研究者进行过系统的研究。而本文的研究是探索其中的理论基础、关键问题和技术的一种尝试。同时,本文的研究将为决策支持系统和知识管理的研究作为积极的贡献,为实现两者的全面系统的集成提供有益的指导。
Managerial decision-making, as a knowledge intensive activity, is always challenging. To make high quality decision, decision-makers need to be good at integrating relative information and knowledge and be equipped with a great deal of experience. Especially under rapid change and complex competition envorinment, making decision correctly and timely is depand on having valuable information and knowledge. People often consider lack of information will lead to failure in make good decision. Actually, the real reason is that there is no knowledge which has integred view and is able to focaste future. Knowledge management has become a focused topic in managerial decision-making and decision support research area. There are great changes in support model of how managers utilize organizational data, inforamtion and knowledge during their decision making. Managers have to face to knowledge overload because of huge amout of knowledge, which leads to organizational knowledge's usage is very low. More seriously, most of organizational knowledge are segmented, isloated, heterogeneous, and distributed, and existed in different sources. This fact is also making providing effective dicision suppore more difficult. Obviously, effective knowledge integration is the basis and premise of solving those problems. Unfortunately, judgment of knowledge value, presentation and integration of tacit knowledge, as the key factor of knowledge integration, is still in progress.
According to questions mentioned above, this paper has carried out some related research works:
1. Building the theory framework of intelligent knowledge integration towards decision making. The research approach of this paper is different from traditional research path which focus on orgnizational behavior, is from naturalistic decision making perspective to discuss decision-maker centred knowledge integration. In order to discuss this question, this paper established a systematic theory framework which based on decision science, cognitive psychology, artificial intelligence, naturalistic decision making, and multi-agent system, and aimed to research intelligent knowledge acquisition, intelligent knowledge representation, intelligent knowledge evaluation, and intelligent knowledge apply. The theory framework is the foundation of this paper's theory analysis and model formulation.
2. Propose a knowledge representation method to describe tacit knowledge depands on causuality relationship and turst relationship. By reviewing and analyzing knowledge representation methods in artificial intelligence, this paper proposed a tacit knowledge representation method based on fuzzy cognitive maps which is a kind of soft computing method. Futhermore, this paper introduce a novel concept-belief of knowledge, the concept is reflecting how expert is confident on they suggestion.
3. Discuss the features of knowledge's value, giving the value funtion of knowledge towords decision making. The real knowledge value for decision-maker is whether the knowledge could decrease degree of uncertainty in decision situation. The function will calculate the concept value, causuality value and trust value to measure the contribution which the target knowledge may bring to decision problem. Based on the value of knowledge, decision-makers could filter knowledge and choose a right decision point. The result is lowering influence of knowledge overload.
4. In order to overcome problem of access knowledge from heterogeneous knowledge sources, an intelligent approach of integrating knowledge based on multi-agent systems was introduced. Under the multi-agent system, this paper presented a new decision support scheme which implemented by sharing mind model in an intelligent agent in order to overcome bounded awareness. The agent uses the sharing mind model to integrate experience knowledge in the multi-agent system.
5. The mechnism of form sharing mind model is according to the approach provided by Evidence Theory. The purpose is fusion experence knowledge from other agents (including intelligent agent and human).
6. Carry out simulation experiments to evaluate and analysis the knowledge value model, multi-agent knowledge integration and shared mind model under Repast simulation system. Design a prototype of intelligent knowledge integration systems in multi-agent JACK platform.
The new ideas contrubuted to theory and practice is listed below:
1. Propose a method of decision problem formulazation according to key factors and their relationship of decision problem. The method converts decision prolem into decision situation.
2. Provide causuality-trusty fuzzy cognitive map approach to representation tacit knowledge. The approach introduced influence of expert's confidence into knowledge properties, which would get better understand of tacit knowledge. The approach also make tacit knowledge could be analyzed quantitatively.
3. Building knowledge value function based on Entropy Theory. The function includes three elements:concept value function, causualty value function and trust value function. Apply the function in processes of decision making would provide effective way to decrease knowledge overload.
4. Establish a framework of intelligent knowledge integration based on RPD-BDI agent and multi-agent system techniques and proposal a novel RPD-CBR reasoning method of agent. This paper introduced individual mind model and sharing mind model to construct agent's knowledge structure. Depand on sharing mind model, agents could absorb experience knowledge from other agents, which help a single agent overcome its bounded awareness.
Above all, intelligent knowledge integration is a challenging and worthy of exploring. Few researchers have applied naturalistic decision making theory and multi-agent system technique togther to study this area. This paper has tried to discuss the theory foundation, key issues, and relavant technology. And hope to provide some benefits on integrating decision support systems and knowledge management.
引文
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