Collaborative query routing is a new paradigm for Web search that treats both established search engines and other publicly available indices as intelligent peer agents in a search network. The approach makes it transparent for anyone to build their own (micro) search engine, by integrating established Web search services, desktop search, and topical crawling techniques. We present the 6S peer network, which uses machine learning techniques to learn about the changing query environment. We show that simple reinforcement learning algorithms are sufficient to detect and exploit semantic locality in the network, resulting in efficient routing and high-quality search results.

It is useful to note that many of the reasons why some otherwise meritorious AI applications fail have nothing to do with the AI per se, but rather, with systems engineering and organizational issues. Some embedded AI systems may work well for years on a software platform that is orphaned and porting it would be prohibitively expensive. The delivered application system might work well, but it could be hard to maintain internally. The system may work according to the sponsor's requirements, but it might not be applied to the part of the problem that delivers the largest economic results; or the system might not produce enough visible organizational benefits to protect it in subsequent budget battles.

Ten years ago I was a junior faculty member in a UK university, doing research into the theoretical foundations of multiagent systems. I enjoyed the research, but not the salary. The opportunity arose to work for a startup company at three times my university salary, and the company had already hired some excellent agent researchers that I knew, respected, and liked from conferences and workshops. The job seemed too good to be true; and of course, it was.

The third international conference on Human-Robot Interaction (HRI-2008) was held in Amsterdam, The Netherland, March 12-15, 2008. The theme of HRI-2008, "Living With Robots", highlights the importance of the technical and social issues underlying human-robot interaction with companion and assistive robots for long-term use in everyday life and work activities. More than two hundred and fifty researchers, practitioners, and exhibitors attended the conference, and many more contributed to the conference as authors or reviewers. HRI-2009 will be held in San Diego, California from March 11-13, 2009.

The goal of the Electric Elves project was to develop software agent technology to support human organizations. We developed a variety of applications of the Elves, including scheduling visitors, managing a research group (the Office Elves), and monitoring travel (the Travel Elves). The Travel Elves were eventually deployed at DARPA, where things did not go exactly as planned. In this article, we describe some of the things that went wrong and then present some of the lessons learned and new research that arose from our experience in building the Travel Elves.

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately.

A generic image learning system, CogniSight, is being used for the inspection of fishes before filleting offshore. Each CogniSight system uses four neural network chips (a total of 312 neurons) based on a natively parallel, hard-wired architecture that performs real-time learning and nonlinear classification (RBF). These systems are trained by the ship crew using Image Knowledge Builder, a "show and tell" interface that facilitates easy training and validation. The fast and high return of investment (ROI) to the fishing fleet has significantly increased the market share of Pisces Industries, the company integrating CogniSight systems to its filleting machines.

Custom DU is an automated underwriting system that enables mortgage lenders to build their own business rules that facilitate assessing borrower eligibility for different mortgage products. By means of the user interface, lenders can also customize their underwriting findings reports, test the rules that they have defined, and publish changes to business rules on a real-time basis, all without any software modifications. The user interface enforces structure and consistency, enabling business users to focus on their underwriting guidelines when converting their business policy to rules. Using Custom DU, lenders can create different rule sets for their products and assign them to different channels of the business, allowing for centralized control of underwriting policies and procedures--even if lenders have decentralized operations.

In this editorial we introduce the articles published in this special AI Magazine issue on innovative applications of artificial intelligence. Discussed are a pick-pack-and-ship warehouse-management system, a neural network in the fishing industry, the use of AI to help mobile phone users, building business rules in the mortgage lending business, automating the processing of immigration forms, and the use of the semantic web to provide access to observational datasets.

The Association for the Advancement of Artificial Intelligence presented the 2007 Fall Symposium Series on Friday through Sunday, November 9–11, at the Westin Arlington Gateway, Arlington, Virginia. The titles of the seven symposia were (1) AI and Consciousness: Theoretical Foundations and Current Approaches, (2) Artificial Intelligence for Prognostics, (3) Cognitive Approaches to Natural Language Processing, (4) Computational Approaches to Representation Change during Learning and Development, (5) Emergent Agents and Socialities: Social and Organizational Aspects of Intelligence, (6) Intelligent Narrative Technologies, and (7) Regarding the "Intelligence" in Distributed Intelligent Systems.