User Models and Scrutable Systems:
How Decision Theory and Abductive Reasoning Support Designing Presentations and Recognizing User Models

Andrew Csinger David Poole
Department of Computer Science
University of British Columbia, Vancouver, Canada
{csinger,poole}@cs.ubc.ca

In building effective multimedia interfaces, a chief limitation of traditional presentation design is the inability to meet individual user expectation at run-time. Recent technology offers new and unexplored possibilities for on-line design of individualized presentations that surpass the limits of the ``one size fits all'' approach forced onto books by the demands of the printing press. Rather than just adding horsepower to traditional techniques, we investigate user modelling strategies for intelligent multimedia interfaces.

A model of the user is needed for run-time determination of form and content. We overview our minimal-AI approach to user modelling, and introduce a working prototype we have built to demonstrate these ideas.

We use a variant of the Theorist framework for hypothetical reasoning [Poo87]. Given formulae (the facts), and set of formulae (the assumables), an explanation of a closed formula is a consistent set that implies , where . Elements of are called assumptions.

This work extends Theorist to incorporate both recognition and design into the same framework (see [CP93]). is partitioned into the set of assumables available for user recognition, and the set of assumables available for presentation design. is partitioned into disjoint sets, where every assumable in is assigned a prior probability ; the disjoint sets correspond to independent random variables (as in [Poo93]). Every assumable in is assigned a nonnegative cost .

A model of the user is the set of recognition assumptions that explains observations about the user. , given independence of recognition partitions. Given model , a design is a set of design assumptions that (together with ) explains the presentation to be made; its cost is the sum of the costs of its constituent assumptions (i.e., ).

Note that the partitioning of partitions each explanation into a model and a design. We define a preference relation over explanations such that: if
or and . So, the ``best'' explanation consists of the most plausible model of the user and the lowest cost presentation.

These reasoning techniques are combined with an interaction paradigm we call scrutability, whereby users critique the model in pursuit of better presentations. We display to users a critical subset of the assumptions the system has made (determined by sensitivity analysis), and permit the user to change values using an intuitive graphical user interface (GUI). When the user explicitly sets the value of an assumption, this new information is a fact. Confidence is increased in the values of other assumptions displayed in the same GUI window as well, because the user was attending to that window and might have seen those values.

Both recognition and design processes are performed at run-time, but are logically separated; this separation should result in easier acquisition and debugging of knowledge.

Our prototype implementation [CB94] demonstrates these ideas in the domain of video authoring. Although our approach to authoring is intended to apply across multiple media, we have begun to demonstrate these ideas with video because authoring in the video medium with traditional approaches inherits and exacerbates the problems from traditional media, and because the popularity of video as a recording medium continues to grow.

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