Representation and Reasoning about Adaptive Interfaces

自适应接口的表示和推理

• 批准号: 0307906
• 负责人: Daniel Weld
• 金额: $ 50.7万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-15 至 2007-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0307906&HistoricalAwards=false
• 关键词: Representation; Reasoning; about; Adaptive; Interfaces

Previous work on adaptive websites, wearable computing and intelligent user interfaces has shown that these tasks present significant challenges to the fields of machine learning, knowledge representation, and reasoning under uncertainty. This project will address the following core artificial intelligence problems using adaptive interfaces as inspiration and an experimental testbed.1) Given a database of behavioral data for one or more users, what is the best representation for encoding a predictive model of user behavior? What are the best algorithms for learning such a model? This project will generalize Markov models and Dynamic Bayes Nets to create Relational Markov Models (RMMs) and Dynamic Probabilistic Relational Models (DPRMs) respectively. Effective inference and learning algorithms will be developed and evaluated against traditional propositional methods.2) Representing user interfaces is a major challenge. This project will extend the work on task-centered user-interface design with ideas from the planning literature (sensory actions, exogenous events) to develop an expressive task formalism with clear semantics.3) Adapting an interface, which is represented as an augmented plan schema, requires new methods for reasoning about actions. In addition to analyzing causal dependency structures, restructuring operations akin to partial evaluation will be necessary. Fast inference is an essential component of this project. A satisficing plan is not good enough, so the work will use a utility model combining plan length with a cognitive dissonance factor. Methodologically, the project is composed of six coupled activities: (1) Formalize the RMM and DPRM representations; (2) Devise efficient particle-filtering inference methods; (3) Develop learning algorithms based on shrinkage; (4) Formalize a declarative, plan-based interface representation, and evaluate expressiveness on a corpus of adaptation examples; (5) Devise a comprehensive set of adaptation transformations and a utility metric; (6) Implement the methods, incorporate in a user interface platform, and perform extensive experiments. The research will have broad impact, because progress in user interfaces has been dwarfed by the simultaneous enormous increase in the speed of computers. Artificial intelligence techniques are perhaps the most promising avenue for harnessing processing power to increase user productivity. This project will contribute to improved user interfaces not only in desktop software but also in personalized information systems for wearable computers.

先前关于自适应网站、可穿戴计算和智能用户界面的工作表明，这些任务对机器学习、知识表示和不确定性推理领域提出了重大挑战。该项目将使用自适应界面作为灵感和实验测试平台来解决以下核心人工智能问题。1）给定一个或多个用户的行为数据数据库，编码用户行为预测模型的最佳表示是什么？学习这种模型的最佳算法是什么？该项目将推广马尔可夫模型和动态贝叶斯网络，分别创建关系马尔可夫模型（RMM）和动态概率关系模型（DPRM）。将开发有效的推理和学习算法，并根据传统的命题方法进行评估。2）表示用户界面是一个重大挑战。该项目将利用规划文献（感官动作、外生事件）中的想法扩展以任务为中心的用户界面设计工作，以开发具有清晰语义的表达性任务形式。3）调整界面，以增强计划的形式表示模式，需要新的方法来推理动作。除了分析因果依赖结构之外，还需要类似于部分评估的重组操作。快速推理是该项目的重要组成部分。令人满意的计划还不够好，因此该工作将使用将计划长度与认知失调因素相结合的实用模型。从方法上来说，该项目由六项耦合活动组成： (1) 正式确定 RMM 和 DPRM 表示； （2）设计高效的粒子过滤推理方法； （3）开发基于收缩的学习算法； (4) 形式化一个声明性的、基于计划的界面表示，并评估适应示例语料库的表达能力； (5) 设计一套全面的适应变换和效用度量； (6) 实现方法，合并到用户界面平台中，并进行广泛的实验。这项研究将产生广泛的影响，因为用户界面的进步与计算机速度的同步巨大增长相形见绌。人工智能技术也许是利用处理能力提高用户生产力的最有前途的途径。该项目不仅将有助于改善桌面软件的用户界面，而且还将有助于改善可穿戴计算机的个性化信息系统。