环绕智能系统中个性化服务技术研究
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摘要
环绕智能(AmI)是普适计算思想的发展,实现信息空间与物理空间的融合,以随时随地提供计算服务,而无需牵扯用户更多精力;同时更加强调智能交互和个性化。个性化技术使AmI能够主动、自适应的根据用户个性特征进行服务,改变原有的标准化服务模式——“One Size Fits All”,真正解决AmI环境中“服务过载”问题,最大程度上满足用户需求,增强用户体验,为构建未来信息社会提供技术支撑。
个性化技术已经在搜索引擎、电子商务、数字图书馆等领域进行了大量的研究与应用,但已有个性化技术难以适合AmI环境。AmI中用户需求因人而异、Context丰富多变,服务众多异构。在由用户、Context、服务构成的高维空间(AmI-Space)中选择满足用户个性化需求的服务系统是极其复杂的。目前,国内外对AmI中个性化服务技术(AmI-PS)研究仍处于初始阶段,缺乏用以指导AmI-PS建设的通用架构及相关技术。
本文根据AmI的特点及其它领域个性化服务研究现状,确定了以下研究内容:
(1)从总体上研究AmI-PS的服务模式和系统架构,对共性功能进行抽象,形成对AmI-PS整体认识,并进行系统的功能划分,用以指导AmI-PS的实现;
(2)研究AmI领域知识和个性化知识的表示,形式化描述出用户的个性特征(即用户有哪些独特之处),形成个性化服务的基础;
(3)研究个性化服务的推理过程,如何在高维的AmI-Space中寻找最优的服务,以满足用户个性需求;
(4)AmI环境中用户个性、Context、服务是动态变化的,为了获得及时准确的服务,需要深入研究AmI-PS的自适应学习能力;
(5)位置信息对于AmI-PS非常重要,现有方法无法满足AmI-PS的定位需求,研究适应AmI特点的定位方法具有重要意义。本文首先对推荐技术、Context-Aware技术、知识表示、Web服务、移动代理、无线定位等AmI-PS相关支撑技术进行综合性研究,为进一步理解和设计系统奠定基础。随后,通过对以上几方面内容的深入研究,解决了AmI-PS中的若干关键问题,取得一定研究成果,主要的贡献与创新如下:
(1)基于自然服务过程,提出分布式推理的个性化服务模式。该模式将个性化过程分为个性化需求推理、需求满足推理、个性化服务参数推理三部分,充分体现个性特征,合理利用计算资源,具有移动性好、健壮性高、可扩展、隐私保护的特点;根据该服务模式对系统共性功能进行抽象和划分,设计出通用的系统框架结构和层次体系结构,描述系统的整体运行机制和模块内部的工作流程,降低系统实现的复杂度,对进一步深入研究各种关键技术起重要的指导作用。
(2)利用OWL本体语言构建了实现AmI知识通用共享的顶层本体和领域本体,提出利用需求本体建立用户和服务之间的桥梁;提出了AmI-HPM层次个性化建模方法,依此构建受多因素影响的个性化需求模型、个性化需求满足模型和个性化服务参数模型。
(3)设计了用户可满足个性化需求推理过程,提出了融合最短路径算法和需求满足度的服务规划理论。用户可满足个性化需求推理方法实现了基于层次个性化模型的推理算法,同时能够完成确定性推理与非确定性推理。服务规划理论首先利用服务需求本体和服务依赖扩展形成服务规划图,再根据由需求匹配度和QoS组成个性需求满足模型计算服务所代表路径的长度,最后利用最短路径算法求得最优服务路径。
(4)提出了基于成对比较和人工神经网络相结合的PC-ANN个性化模型权重学习算法。利用用户直观简便的成对分析的方法快速构建模型权重,克服ANN长时间的监督式训练过程;又能够根据用户反馈差异,进行快速适应用户个性特征的变化;同时提出了用户习惯、偏好、兴趣等经典单因素评价函数的学习算法。
(5)提出了AmI-MHCL多跳协作定位算法和LSEAO最小二乘法误差分析优化算法。AmI-MHCL利用多跳锚点协作定位,能有效提高低密度锚点时的定位率,特别是针对网络边缘的节点定位;LSEAO利用最小二乘法的误差分析,在计算位置同时还给出定位误差范围,以误差为依据对定位锚点进行最优化选择,大大降低误差传递的影响,显著提高定位精度。解决了AmI-PS中众多实体需要低成本、低能耗、Indoor高精度定位,满足了服务过滤,冲突检测,Context推理,个性化推理需要。
(6)以家庭为物理空间搭建了AmI-PS原型系统—AmI-H。首先,利用本文所提理论与方法实现了原型系统,提供具有代表性的温度控制和个性化TV推荐服务,充分体现用户个性特征。由此验证了所提理论的正确性和有效性,同时为其它AmI-PS系统的开发提供了参考。
本文针对AmI的特点首先进行总体研究形成对AmI-PS的整体规划,然后逐一详细研究了相关关键技术,使得AmI-PS根据丰富多变Context和用户个性特征能够主动、自适应、高效、准确地满足用户需求,增强用户体验,具有重大理论和现实意义。
Deriving from pervasive computing concept, Ambient Intelligence(AmI) fusescyberspace and physical space to provide computing services anytime and anywherein a nonintrusive way, (which means) that users are assisted proactively by a digitalenvironment surrounding them. Furthermore, AmI emphasizes on intelligentinteraction and personalization. Personalization of AmI transforms the traditionalstandardized service model,“one size fits all”, into a proactive, adaptive anduser-oriented service model, solves the problem of“overload services”, satisfies theuser need to a maximum extent, enhances the user experience, and provides keytechnical support for building the future information society.
Although personalization has been researched massively and applied in the fieldsof searching engine, e-commerce, digital libraries, etc., those existing techonologiesare difficult to adapt to AmI enviorment in which user need is different from person toperson, context varies dynamically, and services are mostly heterogeneous. Therefor,it is extremely complicated to construct a service system to meet personalized userneed in high dimension AmI-Space composed of user, context and service. At present,the research of AmI personalization is still in its infancy, both at home and abroad,asking for a general architecture as well as related technologies to guide theimplementation of AmI-PS.
According to state-of-the-art of AmI and personalized servicing in other fields,this dissertation mainlyfocuses on the following research contents:
(1) Based on abstract of common function features, the research on service modeand systemic architecture achieves the unified understanding of AmI-PS to lead thedevelopment of AmI-PS;
(2) Study on domain knowledge and personalized knowledge representation inAmI. User's individual characteristics can be formally described to form the basis forpersonalized servicing;
(3) Study on reasoning techniques to find the optimal services for meeting userneeds best;
(4) Study on learning techniques for adapting dynamic changes of AmI such asusers, context, services, to provide services timely and accurately;
(5) Location information is very critical for AmI-PS. Due to the lack ofpositioning techniques adapting to AmI, study of a novel positioning approaches hasan important role in AmI-PS.
This dissertation first researches comprehensively on recommendationtechnology, context awareness technology, knowledge representation, web service,mobile agent, wireless location, etc. as related supporting technologies of AmI-PS tounderstand and design the system better. Then several key issues of AmI-PS aresolved through above aspects of deeply researching. The main contributions andinnovations are as follows:
(1) Simulating user’s natural personalized servicing process, a novel distributedservice model is proposed to divide AmI-PS process into three stages: needsreasoning, needs meet reasoning, personalized servicing parameter reasoning. It haslots of virtues as following: fully showing personalization, high usage of distributedcomputing resource, mobility, robustness, extensibility, privacy protection. Based onabstracting and partitioning common system functionality, a general systemframework and hierarchy structure are proposed to describe the system's overalloperating mechanism and work processes of internal module and reduce thecomplexity of the system implementation, laying a foundation of other keytechniques.
(2) Top ontology and domain ontology are implemented in OWL to represent theshare knowledge of AmI-PS. need ontology is used to associate users with services.AmI-HPM personalized approach is proposed to model personalized information bymany factors such as personalized need model, individual need satisfaction model andpersonalized servicing parameters model.
(3) Through fusing shortest-path algorithm and degree of need satisfaction, apersonalized servicing planning theory is proposed to realize personalized reasoningwith the abilityof uncertain reasoning and deterministic reasoning. Firstly it forms thegraph of services by service demand ontology and service dependence. Then optimalservice path is found through computing the length of service path by compositiondegree of need-matching and QOS of service.
(4) On combination of pairwise comparison and ANN, PC-ANN personalizedmodel weight learning algorithm is proposed. Leveraging user intuitive andconvenient method of pairs-wise analysis to rapidly build model weight, it not only overcome ANN’s long supervised training process, but also quickly adapts to changesof user's characteristics according to user feedback. Furthermore, some classic singlefactors evaluation functions are raised including user habits, preferences and interests.
(5) Multi-hops collaboration positioning algorithm (AmI-MHCL) and locationoptimization algorithm based on Least Squares error analysis (LSEAO) are proposed.AmI-MHCL uses multi-hops anchors to collaborate positioning, increasingpositioning ratio in low-density-anchors situation, especially for nodes at networkedge. Utilizing least squares error analysis, LSEAO could compute position with errorrange to reduce the impact of error propagation significantly and increase positioningaccuracy.
(6) AmI-H is designed and implemented as AmI-PS prototype system in familyscenario. Firstly, AmI-H provides representative temperature control and personalizedTV recommendation service. Applying above proposed theory and approaches it fullyreflect the characteristics of the user. The rationality and correctness of theory andapproaches proposed are validated, in addition it provides a reference model for otherAmI-PS development.
In this dissertation, key techniques of AmI from overall framework to detailapproaches are researched to enable AmI-PS meeting user personalized needs andenhancing user’s experience in an accurate and efficient way. It adapts the traits ofAmI in which users, context, services all are massive, changing, and diverse. Theresults of research have great theoretical and practical significance to the developmentof future information society.
个性化技术已经在搜索引擎、电子商务、数字图书馆等领域进行了大量的研究与应用,但已有个性化技术难以适合AmI环境。AmI中用户需求因人而异、Context丰富多变,服务众多异构。在由用户、Context、服务构成的高维空间(AmI-Space)中选择满足用户个性化需求的服务系统是极其复杂的。目前,国内外对AmI中个性化服务技术(AmI-PS)研究仍处于初始阶段,缺乏用以指导AmI-PS建设的通用架构及相关技术。
本文根据AmI的特点及其它领域个性化服务研究现状,确定了以下研究内容:
(1)从总体上研究AmI-PS的服务模式和系统架构,对共性功能进行抽象,形成对AmI-PS整体认识,并进行系统的功能划分,用以指导AmI-PS的实现;
(2)研究AmI领域知识和个性化知识的表示,形式化描述出用户的个性特征(即用户有哪些独特之处),形成个性化服务的基础;
(3)研究个性化服务的推理过程,如何在高维的AmI-Space中寻找最优的服务,以满足用户个性需求;
(4)AmI环境中用户个性、Context、服务是动态变化的,为了获得及时准确的服务,需要深入研究AmI-PS的自适应学习能力;
(5)位置信息对于AmI-PS非常重要,现有方法无法满足AmI-PS的定位需求,研究适应AmI特点的定位方法具有重要意义。本文首先对推荐技术、Context-Aware技术、知识表示、Web服务、移动代理、无线定位等AmI-PS相关支撑技术进行综合性研究,为进一步理解和设计系统奠定基础。随后,通过对以上几方面内容的深入研究,解决了AmI-PS中的若干关键问题,取得一定研究成果,主要的贡献与创新如下:
(1)基于自然服务过程,提出分布式推理的个性化服务模式。该模式将个性化过程分为个性化需求推理、需求满足推理、个性化服务参数推理三部分,充分体现个性特征,合理利用计算资源,具有移动性好、健壮性高、可扩展、隐私保护的特点;根据该服务模式对系统共性功能进行抽象和划分,设计出通用的系统框架结构和层次体系结构,描述系统的整体运行机制和模块内部的工作流程,降低系统实现的复杂度,对进一步深入研究各种关键技术起重要的指导作用。
(2)利用OWL本体语言构建了实现AmI知识通用共享的顶层本体和领域本体,提出利用需求本体建立用户和服务之间的桥梁;提出了AmI-HPM层次个性化建模方法,依此构建受多因素影响的个性化需求模型、个性化需求满足模型和个性化服务参数模型。
(3)设计了用户可满足个性化需求推理过程,提出了融合最短路径算法和需求满足度的服务规划理论。用户可满足个性化需求推理方法实现了基于层次个性化模型的推理算法,同时能够完成确定性推理与非确定性推理。服务规划理论首先利用服务需求本体和服务依赖扩展形成服务规划图,再根据由需求匹配度和QoS组成个性需求满足模型计算服务所代表路径的长度,最后利用最短路径算法求得最优服务路径。
(4)提出了基于成对比较和人工神经网络相结合的PC-ANN个性化模型权重学习算法。利用用户直观简便的成对分析的方法快速构建模型权重,克服ANN长时间的监督式训练过程;又能够根据用户反馈差异,进行快速适应用户个性特征的变化;同时提出了用户习惯、偏好、兴趣等经典单因素评价函数的学习算法。
(5)提出了AmI-MHCL多跳协作定位算法和LSEAO最小二乘法误差分析优化算法。AmI-MHCL利用多跳锚点协作定位,能有效提高低密度锚点时的定位率,特别是针对网络边缘的节点定位;LSEAO利用最小二乘法的误差分析,在计算位置同时还给出定位误差范围,以误差为依据对定位锚点进行最优化选择,大大降低误差传递的影响,显著提高定位精度。解决了AmI-PS中众多实体需要低成本、低能耗、Indoor高精度定位,满足了服务过滤,冲突检测,Context推理,个性化推理需要。
(6)以家庭为物理空间搭建了AmI-PS原型系统—AmI-H。首先,利用本文所提理论与方法实现了原型系统,提供具有代表性的温度控制和个性化TV推荐服务,充分体现用户个性特征。由此验证了所提理论的正确性和有效性,同时为其它AmI-PS系统的开发提供了参考。
本文针对AmI的特点首先进行总体研究形成对AmI-PS的整体规划,然后逐一详细研究了相关关键技术,使得AmI-PS根据丰富多变Context和用户个性特征能够主动、自适应、高效、准确地满足用户需求,增强用户体验,具有重大理论和现实意义。
Deriving from pervasive computing concept, Ambient Intelligence(AmI) fusescyberspace and physical space to provide computing services anytime and anywherein a nonintrusive way, (which means) that users are assisted proactively by a digitalenvironment surrounding them. Furthermore, AmI emphasizes on intelligentinteraction and personalization. Personalization of AmI transforms the traditionalstandardized service model,“one size fits all”, into a proactive, adaptive anduser-oriented service model, solves the problem of“overload services”, satisfies theuser need to a maximum extent, enhances the user experience, and provides keytechnical support for building the future information society.
Although personalization has been researched massively and applied in the fieldsof searching engine, e-commerce, digital libraries, etc., those existing techonologiesare difficult to adapt to AmI enviorment in which user need is different from person toperson, context varies dynamically, and services are mostly heterogeneous. Therefor,it is extremely complicated to construct a service system to meet personalized userneed in high dimension AmI-Space composed of user, context and service. At present,the research of AmI personalization is still in its infancy, both at home and abroad,asking for a general architecture as well as related technologies to guide theimplementation of AmI-PS.
According to state-of-the-art of AmI and personalized servicing in other fields,this dissertation mainlyfocuses on the following research contents:
(1) Based on abstract of common function features, the research on service modeand systemic architecture achieves the unified understanding of AmI-PS to lead thedevelopment of AmI-PS;
(2) Study on domain knowledge and personalized knowledge representation inAmI. User's individual characteristics can be formally described to form the basis forpersonalized servicing;
(3) Study on reasoning techniques to find the optimal services for meeting userneeds best;
(4) Study on learning techniques for adapting dynamic changes of AmI such asusers, context, services, to provide services timely and accurately;
(5) Location information is very critical for AmI-PS. Due to the lack ofpositioning techniques adapting to AmI, study of a novel positioning approaches hasan important role in AmI-PS.
This dissertation first researches comprehensively on recommendationtechnology, context awareness technology, knowledge representation, web service,mobile agent, wireless location, etc. as related supporting technologies of AmI-PS tounderstand and design the system better. Then several key issues of AmI-PS aresolved through above aspects of deeply researching. The main contributions andinnovations are as follows:
(1) Simulating user’s natural personalized servicing process, a novel distributedservice model is proposed to divide AmI-PS process into three stages: needsreasoning, needs meet reasoning, personalized servicing parameter reasoning. It haslots of virtues as following: fully showing personalization, high usage of distributedcomputing resource, mobility, robustness, extensibility, privacy protection. Based onabstracting and partitioning common system functionality, a general systemframework and hierarchy structure are proposed to describe the system's overalloperating mechanism and work processes of internal module and reduce thecomplexity of the system implementation, laying a foundation of other keytechniques.
(2) Top ontology and domain ontology are implemented in OWL to represent theshare knowledge of AmI-PS. need ontology is used to associate users with services.AmI-HPM personalized approach is proposed to model personalized information bymany factors such as personalized need model, individual need satisfaction model andpersonalized servicing parameters model.
(3) Through fusing shortest-path algorithm and degree of need satisfaction, apersonalized servicing planning theory is proposed to realize personalized reasoningwith the abilityof uncertain reasoning and deterministic reasoning. Firstly it forms thegraph of services by service demand ontology and service dependence. Then optimalservice path is found through computing the length of service path by compositiondegree of need-matching and QOS of service.
(4) On combination of pairwise comparison and ANN, PC-ANN personalizedmodel weight learning algorithm is proposed. Leveraging user intuitive andconvenient method of pairs-wise analysis to rapidly build model weight, it not only overcome ANN’s long supervised training process, but also quickly adapts to changesof user's characteristics according to user feedback. Furthermore, some classic singlefactors evaluation functions are raised including user habits, preferences and interests.
(5) Multi-hops collaboration positioning algorithm (AmI-MHCL) and locationoptimization algorithm based on Least Squares error analysis (LSEAO) are proposed.AmI-MHCL uses multi-hops anchors to collaborate positioning, increasingpositioning ratio in low-density-anchors situation, especially for nodes at networkedge. Utilizing least squares error analysis, LSEAO could compute position with errorrange to reduce the impact of error propagation significantly and increase positioningaccuracy.
(6) AmI-H is designed and implemented as AmI-PS prototype system in familyscenario. Firstly, AmI-H provides representative temperature control and personalizedTV recommendation service. Applying above proposed theory and approaches it fullyreflect the characteristics of the user. The rationality and correctness of theory andapproaches proposed are validated, in addition it provides a reference model for otherAmI-PS development.
In this dissertation, key techniques of AmI from overall framework to detailapproaches are researched to enable AmI-PS meeting user personalized needs andenhancing user’s experience in an accurate and efficient way. It adapts the traits ofAmI in which users, context, services all are massive, changing, and diverse. Theresults of research have great theoretical and practical significance to the developmentof future information society.
引文
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