Multimodal interfaces are becoming increasingly important with the advent of mobile devices, accessibility considerations, and novel software technologies that combine diverse interaction media. This thesis investigates systems support for web browsing in a multimodal interface. Specifically, we outline the design and implementation of a software framework that integrates hyperlink and voice interaction. This enables the user to engage in out-of-turn interactions to personalize access at an information site. For the developer, the framework enables the creation of sites that adapt to the needs of users, yet permits fine-grained control over what interactions to support. Design methodology, implementation details, and two case studies are presented. / Master of Science

"This thesis presents learning and interaction algorithms to support a human teaching hierarchical task models to a robot using a single or multiple examples in the context of a mixed-initiative interaction with bi-directional communication. Our first contribution is an approach for learning a high level task from a single example using the bottom-up style. In particular, we have identified and implemented two important heuristics for suggesting task groupings and repetitions based on the data flow between tasks and on the physical structure of the manipulated artifact. We have evaluated our heuristics with users in a simulated environment and shown that the suggestions significantly improve the learning and interaction. For our second contribution, we extended this interaction by enabling users to teaching tasks using the top-down teaching style in addition to the bottom-up teaching style. Results obtained in a pilot study show that users utilize both the bottom-up and the top-down teaching styles to teach tasks. Our third contribution is an algorithm that merges multiple examples when there are alternative ways of doing a task. The merging algorithm is still under evaluation. "