Tutorials

Both tutorials will be given on September 10.

Participation at the conference entitles you to participating at any of the tutorials for no extra charge.

T1: Visual Analytics: A New Cross-Disciplinary Field of Research

Duration:

Half-day (Sep 10, afternoon)

Tutors:

Gennady Andrienko and Natalia Andrienko

Abstract:

Visual analytics is an emerging field of cross-disciplinary research where AI scientists can make a valuable contribution. The goal of this tutorial is to make the AI community aware of the new field, its major research challenges, and the existing background and in this way stimulate AI scientists to participate in the visual analytics research in cooperation with other specialists, in particular, researchers dealing with visual representations and interaction technologies. The tutorial will present some state-of-the-art software tools devised to support data exploration and analysis. The tools combine interactive visualisation with data transformations and data mining methods. It is planned to conclude the tutorial with a discussion of possible interactions and synergies between visualisation and AI and potential contribution of AI into the development of visual analytics.

The program of the tutorial includes
1. Introduction of visual analytics
2. State of the art in visual representations and interaction technologies
3. Scalability challenge
4. Knowledge capture challenge
5. Visualisation and AI

Tutor Information:

Gennady and Natalia Andrienko are research fellows at the Fraunhofer Institute Intelligent Analysis- and Information Systems (IAIS). They have published extensively on the subject, including their latest monograph "Exploratory Analysis of Spatial and Temporal Data" (published in December 2005 by Springer). They have been involved in numerous international research projects.

T2: Preference Modeling, Elicitation, Representation, and Reasoning

Preliminary material about this tutorial

is available here

Duration:

Half-day (Sep 10, morning)

Tutor:

Carmel Domshlak (material prepared jointly with Ronen Brafman)

Abstract:

When we design an agent that automatically shops on the web or controls a rover on Mars, we don't want it to buy any item or conduct any experiment. We want it to buy the best available item and conduct the most useful experiment. In short, we want it to act optimally. But acting optimally on behalf of a user requires understanding of that user's goals and preferences. How can an agent obtain this information efficiently when acting on behalf of a lay user? How can this be done with a minimal effort on the part of the user? How does one represent preference information compactly and reasons with it effectively? These questions drive the research conducted in the area of preference modeling, elicitation, representation, and reasoning techniques. The tutorial will survey some of the major developments in this area, discussing the problems of decision-making under certainty and uncertainty, and explaining some practical applications of each of these settings and their characteristics. Much emphasis will be placed on graphical models of preference and models of qualitative preferences that are especially suitable for lay users, as well as on algorithmic techniques for preference elicitation and reasoning.

Tutor Information:

Carmel Domshlak is a Senior Lecturer at the Faculty of Industrial Engineering and Management in Technion, Haifa, Israel. His research interests are in modeling and reasoning about preferences, planning, and reasoning about action. He is a member of the JAIR editorial board.