Decision-Theoretic Methods for Personalized Adaptive Information Selection and Display

Stanford University, Stanford CA

Investigators

Abstract

The number and size of simultaneously accessible information sources available to users continuously grows with advances in information technology, dramatically increasing the information processing burden on decision makers at all levels. Dynamically focusing user attention to a manageable subset of information that is "most valuable" at a given situation and time, and properly displaying this information, could have a crucial impact on the quality of the decisions s/he makes. To address this challenging problem, we need to make systems aware of and responsive to the general and current needs and preferences of the user. In this project, the PI will develop new preference elicitation and reasoning technology for use in large-scale information integration and decision support systems that provide user-oriented selection, transformation, and integration of information arriving from multiple, heterogeneous, dynamically changing information sources. The goal is to support each user's personalized information needs while avoiding information overload yet remaining responsive to the user's changing interests and context, and to the available data sources in near real time. To this end, the PI will exploit and enhance recent developments in graphical qualitative models for preference representation, algorithms for constrained optimization, knowledge compilation strategies, and decision-theoretic frameworks for information processing and integration. He will build on his team's extensive prior work in the areas of decision-theoretic artificial intelligence, modeling and reasoning about people's decision strategies, and human-computer interaction. Effectiveness of the new technology will be demonstrated in an interactive application for field biologists. Broader Impacts: The novel preference-based presentation techniques to be developed in this research will in the short run significantly aid field biologists-who gather large quantities of heterogeneous information while in the field but have limited methods for working with this information-by providing them with richer capture and comprehension of information without overload. In the longer run, these same user interface techniques will be of value across other scientific disciplines as well. Furthermore, they will be of value in diverse applications such as helping managers monitor information about their organization in real time, or helping rescue teams make time-critical decisions by providing team members with information that is sensitive to their current context, their role, and the team's state. More generally, this work will help people effectively utilize the abundant information available to them without drowning in it.