Develop an abstract representation of information which is modality  independent.

Develop criteria for presentation of information in different modalities.

Extending statistical Language Models to robustly map multimodal inputs into internal representations in the ISU approach.

Reconfiguration by “plugging in” task and domain descriptions.

Can we reuse existing domain ontologies?

Explore the suitability of different knowledge representations for generation of multimodal dialogue systems.

Plug-and-play technology for devices and services.

Explore the relationship between domain processes and dialogue processes.

Generating user-tailored textual, tabular, or graphical presentations of information.

Sometimes in parallel with other modalities.

What is the best abstract representation of information committed to during a dialogue?

For each user and task, what information should  be presented and what is its best mode of presentation?

Adapting to different users – their knowledge and preferences.

Multiple dialogue strategies available to the system, chosen depending on context.

Reinforcement Learning applied to the problem of automatic strategy optimisation.

What representations are most suited to adaptivity and learning?

What reward functions can be developed for learning about dialogue management?