Representation and Reasoning about Adaptive Interfaces

University Of Washington, Seattle WA

Investigators

Abstract

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.