自适应人机界面的模型设计

1.引言对于计算机系统,传统的设计方法很少考虑用户的使用效率方面的问题.在人机界面的研究方面投入甚少.使设计出的系统功能复杂.普通用户不能  

User-centered approach to adaptive interfaces

Both adaptive and adaptable user interfaces are intended to fit the needs of individual users and their tasks better. A problem with these interfaces is that users must have and use additional knowledge, either to understand the automatic, system-driven adaptations or to adapt the interface on their own. Beyond these two extreme approaches, an automatically self-adapting system and a user manually adapting the system, the approach introduced in this paper strives for a third option, in which users are able to tell the system how to adapt itself. Accordingly, methods include adaptive adaptation (i.e. modifying the adaptation strategies) and implicit adaptation (i.e. changing the underlying models). The applicability of these findings is illustrated in the context of an adaptive action prompting environment.  

情境感知驱动的移动设备自适应用户界面模型

为降低移动设备控制与对话中的用户认知负荷和操作难度，采取基于情境感知的自适应人机交互策略，分析其中的交互机制和可视化形式，建立用户界面模型指导界面设计和原型构建。该模型制定了原始情境数据的分类感知方式、作用机制和结构化组织形式，设定不同的自适应控制策略和情境触发规则，对自适应交互状态转移过程进行约束。形式化定义自适应过程中的界面模式及其转换函数，并提出重构、替代、几何变换、过滤和推送等具体可视化表达形式。根据用户反馈信息选取显性和隐性评价指标，构造评价函数评估自适应效果，对自适应机制进行相应调整与优化。应用此模型进行自适应手机名片夹界面的视觉设计和原型开发，测试结果表明，自适应界面能有效提高用户操作效率和主观满意度，进一步验证了模型的可行性与有效性。  

自适应笔式用户界面开发的概念框架

提出了一个指导自适应笔式用户界面开发的概念框架CFAPUI(a conceptual framework for developing adaptive pen-based user interface).该框架详细描述了自适应笔式用户界面的软件体系结构,阐述了自适应笔式用户界面的软件体系结构中的各组成模块及其相互间的关系.同时,在体系结构的基础上提出自适应笔式用户界面开发时的具体过程步骤和方法,并通过一个笔式表单应用来说明该开发框架在指导自适应笔式用户界面开发中的具体应用.  

There is more to context than location

Context is a key issue in interaction between human and computer, describing the surrounding facts that add meaning. In mobile computing location is usually used to approximate context and to implement context-aware applications. We propose that ultra-mobile computing, characterized by devices that are operational and operated while on the move (e.g. PDAs, mobile phones, wearable computers), can significantly benefit from a wider notion of context. To structure the field we introduce a working model for context, discuss mechanisms to acquire context beyond location, and application of context-awareness in ultra-mobile computing. We investigate the utility of sensors for context-awareness and present two prototypical implementations — a light-sensitive display and an orientation-aware PDA interface. The concept is then extended to a model for sensor fusion to enable more sophisticated context recognition. Based on an implementation of the model an experiment is described and the feasibility of the approach is demonstrated. Further, we explore fusion of sensors for acquisition of information on more sophisticated contexts.  

动态调度系统中自适应用户界面的设计与实现

该文以炼化企业的动态调度系统为应用背景，以面向动态调度的自适应用户界面的设计与实现为主要研究内容，分析了当前炼化企业动态调度系统用户操作的流程以及界面的需求并建立起相应的数学模型。借鉴自适应用户界面的相关研究成果，设计了自适应用户界面的体系结构以及用户的动态行为过程，针对流程行业的应用特点提出了利用产生式推理和基于上下文的用户意图捕捉的方法，最后给出应用实例。文中所述方法已经成功在企业中得到应用。使用结果表明，该方法能够有效地开发动态调度系统的自适应用户界面，获得满意效果。  

Design and evaluation of an adaptive virtual guide for Web applications

Most applications accessible through the Web suffer from a noticeable lack of support for adapting to the different information needs that different users may have regarding a certain topic. However, completely automatic adaptive support can still be confusing for users who may not understand the reasons for the dynamic change in the behaviour of an application. In this paper, we present a possible solution to provide adaptive support that does not disorient the user. The solution integrates a virtual January 18, assistant that is able to provide adaptive support in an adaptable application. We discuss an example of the application of this approach involving the support of Web visits to virtual museums. We then present the results of an empirical usability test of such an application. Published online: 29 January 2002  

On the role of individual human abilities in the design of adaptive user interfaces for scientific problem solving environments

A scientific problem solving environment should be built in such a way that users (scientists) might exploit underlying technologies without a specialised knowledge about available tools and resources. An adaptive user interface can be considered as an opportunity in addressing this challenge. This paper explores the importance of individual human abilities in the design of adaptive user interfaces for scientific problem solving environments. In total, seven human factors (gender, learning abilities, locus of control, attention focus, cognitive strategy and verbal and nonverbal IQs) have been evaluated regarding their impact on interface adjustments done manually by users. People’s preferences for different interface configurations have been investigated. The experimental study suggests criteria for the inclusion of human factors into the user model guiding and controlling the adaptation process. To provide automatic means of adaptation, the Intelligent System for User Modelling has been developed. Elena Zudilova-Seinstra is a Senior Researcher at the Scientific Visualisation and Virtual Reality Group of the University of Amsterdam. Previously, she worked for the Corning Scientific Centre. Apart from being a researcher, in 1999–2002 she was a part-time Assistant Professor at the St. Petersburg Academy of Management Methods and Techniques. She received her M.S. degree in technical engineering in 1993 and Ph.D. in computer science in 1998 from the St. Petersburg State Technical University. In 1996, she received an award for R&D from the Welles-Johnson Foundation of Maryland. She is a Program Committee Member of several International Conferences and Workshops. Her current research interests include multi-modal and adaptive interaction, scientific visualisation, virtual and augmented reality, ambient intelligence and usability studies. She has more than 40 research publications and three editorials in these areas. Also, she has been an INTAS evaluator since February 2005.  

Using shared representations to improve coordination and intent inference

In groupware, users must communicate about their intentions and aintain common knowledge via communication channels that are explicitly designed into the system. Depending upon the task, generic communication tools like chat or a shared whiteboard may not be sufficient to support effective coordination. We have previously reported on a methodology that helps the designer develop task specific communication tools, called coordinating representations, for groupware systems. Coordinating representations lend structure and persistence to coordinating information. We have shown that coordinating representations are readily adopted by a user population, reduce coordination errors, and improve performance in a domain task. As we show in this article, coordinating representations present a unique opportunity to acquire user information in collaborative, user-adapted systems. Because coordinating representations support the exchange of coordinating information, they offer a window onto task and coordination-specific knowledge that is shared by users. Because they add structure to communication, the information that passes through them can be easily exploited by adaptive technology. This approach provides a simple technique for acquiring user knowledge in collaborative, user-adapted systems. We document our application of this approach to an existing groupware system. Several empirical results are provided. First, we show how information that is made available by a coordinating representation can be used to infer user intentions. We also show how this information can be used to mine free text chat for intent information, and show that this information further enhances intent inference. Empirical data shows that an automatic plan generation component, which is driven by information from a coordinating representation, reduces coordination errors and cognitive effort for its users. Finally, our methodology is summarized, and we present a framework for comparing our approach to other strategies for user knowledge acquisition in adaptive systems.  

Programming by Demonstration Using Version Space Algebra

Programming by demonstration enables users to easily personalize their applications, automating repetitive tasks simply by executing a few examples. We formalize programming by demonstration as a machine learning problem: given the changes in the application state that result from the user's demonstrated actions, learn the general program that maps from one application state to the next. We present a methodology for learning in this space of complex functions. First we extend version spaces to learn arbitrary functions, not just concepts. Then we introduce the version space algebra, a method for composing simpler version spaces to construct more complex spaces. Finally, we apply our version space algebra to the text-editing domain and describe an implemented system called SMARTedit that learns repetitive text-editing procedures by example. We evaluate our approach by measuring the number of examples required for the system to learn a procedure thatworks on the remainder of examples, and by an informal user study measuring the effort users spend using our system versus performing the task by hand. The results show that SMARTedit is capable of generalizing correctly from as few as one or two examples, and that users generally save a significant amount of effort when completing tasks with SMARTedit's help.