Articles in this issue describe recent approaches for integrating algorithms and data structures from diverse subfields of AI. The new applications and significant improvements to existing applications this work has enabled demonstrates the ability of integrated systems and research to continue progress towards human-level artificial intelligence.

This special issue is based on the premise that in order to achieve human-level artificial intelligence researchers will have to find ways to integrate insights from multiple computational frameworks and to exploit insights from other fields that study intelligence. Articles in this issue describe recent approaches for integrating algorithms and data structures from diverse subfields of AI. Much of this work incorporates insights from neuroscience, social and cognitive psychology or linguistics. The new applications and significant improvements to existing applications this work has enabled demonstrates the ability of integrated systems and research to continue progress towards human-level artificial intelligence.

The NIPS 2003 workshops included a feature selection competition organized by the authors. We provided participants with five datasets from different application domains and called for classification results using a minimal number of features. The competition took place over a period of 13 weeks and attracted 78 research groups. Participants were asked to make online submissions on the validation and test sets, with performance on the validation set being presented immediately to the participant and performance on the test set presented to the participants at the workshop. In total 1863 entries were made on the validation sets during the development period and 135 entries on all test sets for the final competition. The winners used a combination of Bayesian neural networks with ARD priors and Dirichlet diffusion trees. Other top entries used a variety of methods for feature selection, which combined filters and/or wrapper or embedded methods using Random Forests, kernel methods, or neural networks as a classification engine. The results of the benchmark (including the predictions made by the participants and the features they selected) and the scoring software are publicly available. The benchmark is available at www.nipsfsc.ecs.soton.ac.uk for post-challenge submissions to stimulate further research.

The NIPS 2003 workshops included a feature selection competition organized by the authors. We provided participants with five datasets from different application domains and called for classification results using a minimal number of features. The competition took place over a period of 13 weeks and attracted 78 research groups. Participants were asked to make online submissions on the validation and test sets, with performance on the validation set being presented immediately to the participant and performance on the test set presented to the participants at the workshop. In total 1863 entries were made on the validation sets during the development period and 135 entries on all test sets for the final competition. The winners used a combination of Bayesian neural networks with ARD priors and Dirichlet diffusion trees. Other top entries used a variety of methods for feature selection, which combined filters and/or wrapper or embedded methods using Random Forests, kernel methods, or neural networks as a classification engine. The results of the benchmark (including the predictions made by the participants and the features they selected) and the scoring software are publicly available. The benchmark is available at www.nipsfsc.ecs.soton.ac.uk for post-challenge submissions to stimulate further research.

The NIPS 2003 workshops included a feature selection competition organizedby the authors. We provided participants with five datasets from different application domains and called for classification resultsusing a minimal number of features. The competition took place over a period of 13 weeks and attracted 78 research groups. Participants were asked to make online submissions on the validation and test sets, with performance on the validation set being presented immediately to the participant and performance on the test set presented to the participants at the workshop. In total 1863 entries were made on the validation sets during the development period and 135 entries on all test sets for the final competition. The winners used a combination of Bayesian neural networkswith ARD priors and Dirichlet diffusion trees. Other top entries used a variety of methods for feature selection, which combined filters and/or wrapper or embedded methods using Random Forests,kernel methods, or neural networks as a classification engine. The results of the benchmark (including the predictions made by the participants and the features they selected) and the scoring software are publicly available. The benchmark is available at www.nipsfsc.ecs.soton.ac.uk for post-challenge submissions to stimulate further research.

We are pleased to publish this special selection of articles from the Sixteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-04), which occurred July 27-29, 2004 in San Jose, California. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. The emerging applications track features technologies that are rapidly maturing to the point of application. The other three articles, which are from the emerging technology track, were selected because they are particularly innovative and show great potential for deployment.

We are pleased to publish this special selection of articles from the Sixteenth Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-04), which occurred July 27-29, 2004 in San Jose, California. IAAI is the premier venue for learning about AI's impact through deployed applications and emerging AI technologies. Case studies of deployed applications with measurable benefits arising from the use of AI technology provide clear evidence of the impact and value of AI technology to today's world. The emerging applications track features technologies that are rapidly maturing to the point of application. The seven articles selected for this special issue are extended versions of the papers that appeared at the conference. Four of the articles describe deployed applications that are already in use in the field. The other three articles, which are from the emerging technology track, were selected because they are particularly innovative and show great potential for deployment.

We are pleased to publish this special selection of papers from the 2003 Innovative Applications of Artificial Intelligence Conference (IAAI-03). IAAI seeks out applications of artificial intelligence that either demonstrate new technology or use previously known technology in innovative ways. IAAI particularly seeks out examples of deployments of AI technology that tackle the problems of demonstrating value and planning for long-term deployment. The five articles we have selected for this special issue are extended versions of papers that appeared in the conference. Two of the articles are deployed applications that have already demonstrated practical value. The remaining three articles are particularly innovative emerging applications. We will briefly outline each of them.

However, what are the application areas include autonomous spacecraft key research topics? There are the scientific disciplines support, failure analysis and on-board diagnosis such as physics and chemistry that develop of vehicle systems, automated generation theories, and there are engineering disciplines of control software for photocopiers, and intelligent aids for learning about thermodynamic that develop solutions that change the cycles. Qualitative reasoning is thus relevant physical world. Both use formal mathematical for researchers who are interested in important systems, as well as computer implementations, AI issues as well as for managers, to derive conclusions about natural and artificial developers, and engineers who are looking for pieces of the world. Does this approach potential industrial benefits of AI. not provide a systematic and formal way to A decade has passed since the publication of reason about the physical world? What remains three collections of papers and a book covering to be done for AI research in this area?

All accepted papers will appear in the conference proceedings published by AAAI Press. Selected authors will be invited to submit extended versions of their Ingrid Russell, University of Hartford papers to a special issue of the International Journal on Artificial Intelligence Tools irussell@hartford.edu The papers Valerie Barr, Hofstra University should not exceed 5 pages and is due by October 24, 2003. All submissions will be done Zdravko Markov, Central Connecticut State electronically via FLAIRS web submission system, which will be available through University the conference website. Please consult the conference web page for details on paper submission.