This issue summarizes the state of the art in structured knowledge transfer, which is an emerging approach to the general problem of knowledge acquisition and reuse. Its goal is to capture, in a general form, the internal structure of the objects, relations, strategies, and processes used to solve tasks drawn from a source domain, and exploit that knowledge to improve performance in a target domain.

This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington's clever use of statistical NL techniques to answer questions across the open web).

This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington’s clever use of statistical NL techniques to answer questions across the open web).

This is the preface to the 26th International Conference on Logic Programming Special Issue

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

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.

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.

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.

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.