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. Developed by Fannie Mae, Custom DU has been used since 2004 by several lenders to automate the underwriting of numerous mortgage products. Custom DU uses rule specification language techniques and a web-based, user-friendly interface for implementing business rules that represent business policy. 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. Once lenders have created their rules, loans are routed to the appropriate rule sets, and customized, but consistent, results are always returned to the lender. 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.

AAAI-10 will be held in Atlanta, Georgia! Please mark your calendars, and visit www.aaai.org/Conferences/AAAI/ For more information on Human and Machine Cognition Chicago, please visit www.choose AAAI recently launched a series of (IHMC), is the sixth recipient of the chicago.com. The AAAI 2008 Teaching Forum aims 2006 and 2007 will continue in 2008, industry representative.

(ICEIS 2008). (ESAS 2008). Fax: 32-26502715 Faculty of Electrical Engineering and Email: ants@iridia.ulb.ac.be Computing, University of Zagreb URL: iridia.ulb.ac.be/ants2008/ Submission requirements may vary for each workshop, but most Multidisciplinary Workshop on Advances in Preference Handling conform to the schedule below.

Crosswords are very popular and represent a useful domain of investigation for modern artificial intelligence. In contrast to solving other celebrated games (such as chess), cracking crosswords requires a paradigm shift towards the ability to handle tasks for which humans require extensive semantic knowledge. This article introduces WebCrow, an automatic crossword solver in which the needed knowledge is mined from the web: clues are solved primarily by accessing the web through search engines and applying natural language processing techniques. In competitions at the European Conference on Artificial Intelligence (ECAI) in 2006 and other conferences this web-based approach enabled WebCrow to outperform its human challengers. Just as chess was once called “the Drosophila of artificial intelligence,” we believe that crossword systems can be useful Drosophila of web-based agents.

The fourth Modeling and Reasoning in Context (MRC) workshop was held on August 20–21, 2007, in conjunction with the Sixth International and Interdisciplinary Conference on Modeling and Using Context, at Roskilde University, Denmark. This year’s workshop included a special track on the role of contextualization in human tasks (CHUT). The overall goal of the workshop was to further the understanding, development, and application of AI methods for context-sensitive information technology.

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.

A generic image learning system, CogniSight, is being used for the inspection of fishes before filleting offshore. More than 30 systems have been deployed on seven fishing vessels in Norway and Iceland over the past three years. 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. Fishers can reinforce the learning anytime when needed. The use of CogniSight has significantly reduced the number of crew members needed on the boats (by up to six persons), and the time at sea has been shortened by 15 percent. 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.

Is it possible to build a conscious machine? There was an almost generally accepted of AI since its beginnings. The symposium was psychological, philosophical, and the first official place where scholars-- neuroscientific theories of consciousness; coming from different fields as far as (3) it is possible to address consciousness neuroscience and philosophy, psychology not only from neuroscience, and computer science--addressed psychology, and philosophy, the issue of consciousness in a but also from AI; and (4) the role of traditional AI environment. Furthermore, embodiment and situatedness is almost there was a good balance of universally recognized. A recurrent topic was the fact that The participants' talks centered on the topic of the symposium and generated the field of consciousness seems to be lively discussions of their research.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29–31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. This year’s workshop emphasized inter-actions and exchange of ideas and experiences between researchers and practitioners whose backgrounds included AI, control theory, systems engineering, software engineering, and related areas.

The Kiva warehouse-management system creates a new paradigm for pick-pack-and-ship warehouses that significantly improves worker productivity. The Kiva system uses movable storage shelves that can be lifted by small, autonomous robots. By bringing the product to the worker, productivity is increased by a factor of two or more, while simultaneously improving accountability and flexibility. A Kiva installation for a large distribution center may require 500 or more vehicles. As such, the Kiva system represents the first commercially available, large-scale autonomous robot system. The first permanent installation of a Kiva system was deployed in the summer of 2006.