There is considerable validity to this point of view: Anyone who develops systems that are intended for use by people can benefit from familiarity with and application of these methods. Accordingly, this article offers a brief introduction to these methods, including examples and suggestions for additional reading (see in particular the Further Reading section). Even people who are already experts in the application of these methods should be aware of potential adaptations and extensions to the methods when applied to systems that are designed to incorporate significant use of AI. The theme articles by Lieberman (2009) and by Jameson (2009) in this issue discuss some of the ways in which systems that incorporate intelligence tend to differ from systems that do not, both in terms of their potential to help users and in terms of possible side effects. These and other properties of intelligent systems can affect the application of design and evaluation methods in various ways, some of which are illustrated in the case studies of this special issue. To organize our discussion, we distinguish broadly three types of activity that are involved in usability engineering: understanding users' needs, interaction design, and evaluation. Except for the fact that understanding users' needs tends to occur early in the design process, these activities generally proceed in parallel and complement each other.

When creating algorithms or systems that are supposed to be used by people, we should be able to adopt a “binocular” view of users’ interaction with intelligent systems: a view that regards the design of interaction and the design of intelligent algorithms as interrelated parts of a single design problem. This special issue offers a coherent set of articles on two levels of generality that illustrate the binocular view and help readers to adopt it.

The goal of the Innovative Applications of Artificial Intelligence (IAAI) conference is to highlight new, innovative, systems and application areas of AI technology and to point out the often-overlooked difficulties involved in deploying complex technology to end users. Those of us who have ventured out of the realm of pure research and tried to build applications to be used by our fellow humans realize that it takes a lot more than just brilliant algorithms to make an application survive in the real world. Each application that succeeds is worth celebrating and the teams behind them are due wholehearted congratulations. It is in this spirit that we bring you this special issue covering select applications from the IAAI conference held last year in Chicago.

The goal of the Innovative Applications of Artificial Intelligence (IAAI) conference is to highlight new, innovative, systems and application areas of AI technology and to point out the often-overlooked difficulties involved in deploying complex technology to end users. Those of us who have ventured out of the realm of pure research and tried to build applications to be used by our fellow humans realize that it takes a lot more than just brilliant algorithms to make an application survive in the real world. Each application that succeeds is worth celebrating and the teams behind them are due wholehearted congratulations. It is in this spirit that we bring you this special issue covering select applications from the IAAI conference held last year in Chicago.

Specialized intelligent systems can be found everywhere: finger print, handwriting, speech, and face recognition, spam filtering, chess and other game programs, robots, et al. This decade the first presumably complete mathematical theory of artificial intelligence based on universal induction-prediction-decision-action has been proposed. This information-theoretic approach solidifies the foundations of inductive inference and artificial intelligence. Getting the foundations right usually marks a significant progress and maturing of a field. The theory provides a gold standard and guidance for researchers working on intelligent algorithms. The roots of universal induction have been laid exactly half-a-century ago and the roots of universal intelligence exactly one decade ago. So it is timely to take stock of what has been achieved and what remains to be done. Since there are already good recent surveys, I describe the state-of-the-art only in passing and refer the reader to the literature. This article concentrates on the open problems in universal induction and its extension to universal intelligence.

AAAI 2008 Workshop Reports

Moving toward true robot autonomy may require new paradigms, hardware, and ways of thinking. The goal of the AAAI 2008 Workshop on Mobility and Manipulation was not only to demonstrate current research successes to the AAAI community but also to road-map future mobility and manipulation challenges that create synergies between artificial intelligence and robotics. The half-day workshop included both a session on the exhibits and a panel discussion. The panel consisted of five prominent researchers who led a discussion of future directions for mobility and manipulation research. Andrew Ng of Stanford University (along with students Ashutosh Saxena and Ellen Klingbeil) focuses on opening arbitrary doors through learning a few visual keypoints, such as the location and type of door handle.

This introduction to AI Magazine's Special Issueon Networks and AI summarizes the seven articles in thespecial issue by characterizing the nature of thenetworks that are the focus of each of the papers.A short tutorial on graph theory and network structuresis included for those less familiar with the topic.

Representations are at the heart of artificial intelligence (AI). This book is devoted to the problem of representation discovery: how can an intelligent system construct representations from its experience? Representation discovery re-parameterizes the state space - prior to the application of information retrieval, machine learning, or optimization techniques - facilitating later inference processes by constructing new task-specific bases adapted to the state space geometry. This book presents a general approach to representation discovery using the framework of harmonic analysis, in particular Fourier and wavelet analysis. Biometric compression methods, the compact disc, the computerized axial tomography (CAT) scanner in medicine, JPEG compression, and spectral analysis of time-series data are among the many applications of classical Fourier and wavelet analysis.

This is a concise and accessible introduction to the field of game theory. The audience for game theory has drastically expanded and now is used in diverse disciplines such as political science, biology, psychology, economics, linguistics, sociology, and computer science. The book covers the main classes of games, their representations, and the main concepts used to analyze them. ISBN 9781598295931, 88 pages.