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.

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. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed. An AI framework has been used to implement an AI module for one of the busiest immigration agencies in the world.

Report on the Seventh International Conference on Case-Based Reasoning

Led by David C. Wilson (University of of usages of generalization in from the University of Ulster. The workshop CBR in robotic soccer, a theme that is researchers and practitioners. The workshops in this year's program were Case-Based An introspective talk, given by David The technical program consisted of fifteen Reasoning and Context-Awareness, W. Aha (Naval Research Lab, USA) papers and eighteen posters. They Case-Based Reasoning in the Health kicked off the event, making attendees are all included in the proceedings Sciences, Textual Case-Based Reasoning: question how case-based reasoning published by Springer. Beyond Retrieval, Uncertainty is perceived by the outside world The first oral session included contributions and Fuzziness in Case-Based Reasoning, and the balance between theoretical in textual CBR, logic-based and Knowledge Discovery and foundations and applied research.

Customer service is defined as the ability of a company to afford the service requestor with the expressed need. Due to the increasing importance of service offerings as a revenue source and increasing competition among service providers, it is important for companies to optimize both the customer experience as well as the associated cost of providing the service. For more complex interactions with higher value, mixed-initiative systems provide an avenue that gives a good balance between the two goals. This article describes a mixed-initiative system that was created to improve customer support for problems customers encountered with their appliances. The tool helped call takers solve customers' problems by suggesting questions aiding the diagnosis of these problems. The mixed-initiative system improved the correctness of the diagnostic process, the speed of the process, and user satisfaction. The tool has been in use since 1999 and has provided more than $50 million in financial benefits by increasing the percentage of questions that could be answered without sending a field service technician to the customers' homes. Another mixed-initiative tool, for answering e-mail from customers, was created in 2000.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing. Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecture has enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing. Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecture has enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

An analog focal-plane processor having a 128 128 photodiode array has been developed for directional edge filtering. It can perform 4 4-pixel kernel convolution for entire pixels only with 256 steps of simple analog processing.Newly developed cyclic line access and row-parallel processing scheme in conjunction with the "only-nearest-neighbor interconnects" architecturehas enabled a very simple implementation. A proof-of-conceptchip was fabricated in a 0.35-m 2-poly 3-metal CMOS technology and the edge filtering at a rate of 200 frames/sec.

We consider interactive tools that help users search for their most preferred item in a large collection of options. In particular, we examine example-critiquing, a technique for enabling users to incrementally construct preference models by critiquing example options that are presented to them. We present novel techniques for improving the example-critiquing technology by adding suggestions to its displayed options. Such suggestions are calculated based on an analysis of users' current preference model and their potential hidden preferences. We evaluate the performance of our model-based suggestion techniques with both synthetic and real users. Results show that such suggestions are highly attractive to users and can stimulate them to express more preferences to improve the chance of identifying their most preferred item by up to 78%.

TEXTAL is a computer program that automatically interprets electron density maps to determine the atomic structures of proteins through X-ray crystallography. Electron density maps are traditionally interpreted by visually fitting atoms into density patterns. This manual process can be time-consuming and error prone, even for expert crystallographers. To automate the process, TEXTAL employs a variety of AI and pattern-recognition techniques that emulate the decision-making processes of domain experts.