Representation and Reasoning about Adaptive Interfaces

先前在自适应网站、可穿戴计算和智能用户界面方面的工作表明，这些任务对机器学习、知识表示和不确定性推理等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，对用户行为的预测模型进行编码的最佳表示是什么？学习这种模型的最佳算法是什么？本计画将利用马尔可夫模型与动态贝氏网路分别建立关系马尔可夫模型与动态机率关系模型。将开发有效的推理和学习算法，并与传统的命题方法进行评估。2）表示用户界面是一个主要的挑战。这个项目将扩展工作的任务为中心的用户界面设计的想法，从规划文献（感官动作，外源事件），开发一个表达任务形式主义与明确的语义。3）适应一个接口，这是表示为一个增强的计划模式，需要新的方法来推理的行动。除了分析因果依赖结构之外，类似于部分评估的重组操作也是必要的。快速推理是这个项目的重要组成部分。一个令人满意的计划是不够好的，所以这项工作将使用一个实用模型相结合的计划长度与认知失调因素。从方法论上讲，该项目由六个耦合活动组成：（1）形式化RMM和DPRM表示;（2）设计有效的粒子过滤推理方法;（3）开发基于收缩的学习算法;（4）形式化声明性，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）设计一套全面的自适应转换和效用度量;（6）实现这些方法，将其纳入用户界面平台，并进行广泛的实验。这项研究将产生广泛的影响，因为与此同时计算机速度的巨大增长相比，用户界面的进步相形见绌。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。该项目将有助于改进不仅是桌面软件的用户界面，而且也有助于改进可佩戴计算机的个性化信息系统。

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.