Invited Speakers

Robots are moving into our everyday life for tasks like entertainment, cleaning, and delivery. To arrive at such systems, a number of key scientific questions must be answered and technological breakthroughs must be accomplished. The areas of service robotics and the RoboCup each define common tasks that allow evaluation of systems promoting integration of robotics and AI. In this talk the application domains are introduced, recent results are reviewed, and issues for future generations are outlined.

Model-based diagnosis techniques have started to enter industrial applications and commercial tools. We focus on pointing out the reasons behind these successes, in terms of both technical solutions and industrial needs. The lessons learned and open problems hampering wider application suggest future theoretical and practical research.

While the study of machine intelligence has focused on the programming of general-purpose computers, digital logic represents a small subset of the latent capability of natural systems to manipulate information. I present some of the remarkable computational tasks that can be performed by the evolution of simple classical and quantum systems, and consider the implications for inference and interfaces of bringing rich sensory information into more conventional computing environments.

Simultaneously with the information "explosion" in the last few decades there has been a corresponding "explosion" in higher education in most countries. This growth in number of students has essentially followed an "extrapolation" of traditional teaching modes. There have, however, been a number of attempts to apply modern electronic tools to promote a change described as "from teaching to learning". In a joint effort Stanford University and selected Swedish universities are promoting a shift towards learning through Learning Laboratories. The talk will illustrate some basic ideas and concepts behind this collaboration and the Learning Laboratories.

For two decades, Bayesian networks constructed by experts have been used in intelligent systems with a fair amount of success. More recently, researchers have developed techniques for constructing Bayesian networks (both parameters and structure) from a combination of expert knowledge and data. These techniques can significantly reduce the cost of building an intelligent system and can be used to identify causal relationships from non-experimental data - an important breakthrough for science. I will describe some of these techniques, concentrating on methods borrowed from Bayesian statistics, and discuss real-world applications.

The optimization methods of operations research and the constraint satisfaction methods of artificial intelligence have a unifying theme: both fields exploit the fundamental and related dualities of search vs. inference and strengthening vs. relaxation. This allows the two fields to be seen as special cases of a more general approach and suggests new methods that fit into neither OR nor AI.

The representation of motion is of central importance in many artificial intelligence-related fields such as robotics, computer graphics, virtual reality, neurophysiology, and so forth. A crucial and not yet completely understood issue is, however, the measurement of motion. Computer vision has proposed a paradigm called "dynamic vision". Within this paradigm, the vast majority of solutions consider a single camera. In this talk we advocate that a pair of uncalibrated cameras should be preferred. The motion measurement and representation issued from such a camera pair are more tractable from a mathematical point of view and can be used in a wider range of applications, such as visual guidance of robots and vehicles, visual surveillance, and virtualized reality.

The rational approach to pharmaceutical drug design begins with an investigation of the relationship between chemical structure and biological activity. Information gained from this analysis is used to aid the design of new or improved drugs. Computational chemists involved in rational drug design routinely use an array of programs to compute geometric and chemical characteristics of molecules. In this talk I describe areas of computer-aided drug design that are important to computational chemists but are also rich in algorithmic problems and have attracted the attention of computer scientists.

Boosting is a general method for producing a very accurate classification rule by combining rough and moderately inaccurate "rules of thumb". While rooted in a theoretical framework of machine learning, boosting has been found empirically to perform rather well. In this talk, I will introduce the Boosting algorithm AdaBoost and explain the underlying theory of boosting, including an explanation of why boosting often does not suffer from overfitting. I also will describe some recent applications of boosting.

This talk presents Multilingual Natural Language Generation (M-NLG), which is proving successful in its attempts to achieve the same goals as machine translation (the more familiar alternative technology for automating multilingual document production) while avoiding many of its pitfalls.

Language processing has a large practical potential when we realize that, for instance, it can be integrated with other modalities made available by a computer. Intelligent interfaces are artifacts that (often) practically embody these concepts. Some prototypes are presented and challenges for the future are discussed.

Mechanism design is the branch of economics and game theory that deals with the design of economic settings and protocols. In this talk we review some of the mechanism design literature and discuss some essential steps in the adaptation of economic mechanisms to non-cooperative computational environments, such as the Internet.