We applied mobile computing to community support and explored mobile computing with a large number of terminals. This article reports on the Second International Conference on Multiagent Systems (ICMAS'96) Mobile Assistant Project that was conducted at an actual international conference for multiagent systems using 100 personal digital assistants (PDAs) and cellular telephones. We supported three types of service: (1) communication services such as e-mail and net news; (2) information services such as conference, personal, and tourist information; and (3) community support services such as forum and meeting arrangements. Participants showed a deep interest in mobile computing for community support.

The 1997 AAAI Workshop on AI Approaches to Fraud Detection and Risk Management brought together over 50 researchers and practitioners to discuss problems of fraud detection, computer intrusion detection, and risk scoring. This article presents highlights, including discussions of problematic issues that are common to these application domains, and proposed solutions that apply a variety of AI techniques.

Much of our intelligence derives from our ability to manipulate our environment through collaborative endeavors. Most extant computer programs and interfaces do little to take advantage of such manifestly human talents and interests, leaving broad avenues of human-computer communication unexplored. In this article, we look at a number of autonomous agent systems that embody their intelligence at least partially through the projection of a believable, engaging, synthetic persona. Among other topics, we touch briefly on samples of research that explore synthetic personality, representations of emotion, societies of fanciful and playful characters, intelligent and engaging automated tutors, and users projected as avatars into virtual worlds.

The emerging literature on combined systems is directed at domains where the prediction of human behavior is not required. Professionals concerned with human outcomes make decisions that are intuitive or analytic or some combination of both. Justifications and methodology are presented for combining analytic and intuitive agents in an expert system that supports professional decision making. The system presented demonstrates the challenges and opportunities inherent in developing and using AI-collaborative technology to solve social problems.

MetLife processes over 260,000 life insurance applications a year. MetLife's intelligent text analyzer (MITA) uses the information-extraction technique of natural language processing to structure the extensive textual fields on a life insurance application. MITA is currently processing 20,000 life insurance applications a month. Eighty-nine percent of the textual fields processed by MITA exceed the established confidence-level threshold and are potentially available for further analysis by domain-specific analyzers.

This article describes a one-day workshop entitled AI and Knowledge Management that was held at the Fourteenth National Conference on Artificial Intelligence. The workshop was successful in identifying areas where AI techniques can be used to help those working on knowledge management and identifying areas for future work in this area.

CHEMREG is a large knowledge-based system used by Air Products and Chemicals, Inc., to support compliance with regulatory requirements for communicating health and safety information in the shipping and handling of chemical products. This article concentrates on one of the knowledge bases in this system: the case-based reasoner. The case-based reasoner addresses the issue of how proper communication of public health and safety information can be ensured while rapid and cost-effective product evaluation is allowed in the absence of actual hazard testing of the product. CHEMREG generates estimates of hazard data for new products from similar products using an existing relational database as a case library.

In recent years, there has been a resurgence in research on empirical methods in natural language processing. These methods employ learning techniques to automatically extract linguistic knowledge from natural language corpora rather than require the system developer to manually encode the requisite knowledge. This article presents an introduction to the series of specialized articles on these topics and attempts to describe and explain the growing interest in using learning methods to aid the development of natural language processing systems.

This article surveys the use of empirical, machine-learning methods for a particular natural language-understanding task-information extraction. The author presents a generic architecture for information-extraction systems and then surveys the learning algorithms that have been developed to address the problems of accuracy, portability, and knowledge acquisition for each component of the architecture.

The Sixth International Workshop on Nonmonotonic Reasoning was held 10 to 12 June 1996 in Timberline, Oregon. The aim of the workshop was to bring together active researchers interested in nonmonotonic reasoning to discuss current research, results, and problems of both a theoretical and a practical nature.