Busey Bank (Champaign, Ill.) is using intelligent-agent technology to launch its Lloyds Bowmaker Motor Finance (Petersfield, U.K.) has implemented a The Philadelphia Stock Exchange care products, has developed a rulebased neural network-based system for credit (Philadelphia, Pa.) has adopted an multinational order-entry and scoring new loan applications. The company is system helps Lloyds determine whether increase the reliability and scalability using the system to process orders to accept a loan and gives the reasons of network-supported options-trading from its network of more than for its choice. The system uses an electronic facilities. The software will permit installed a rule-based expert system to camera to image the front face of letters, team members in different geographic manage the complexity of producing identify the destination address, locations to explore similar multisensory more than 20,000 new designs and and determine its delivery-point bar environments both independently 2.4 billion greeting cards annually. The company has completely reengineered its operation, converting an Telecommunications providers MCI Healthcare software developer HBO & antiquated job-shop operation into a (Washington, D.C.) and BT (London, Company (Atlanta, Ga.) is developing state-of-the-art cellular one.

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. The current special issue reviews recent research in empirical methods in speech recognition, syntactic parsing, semantic processing, information extraction, and machine translation. 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.

One of the best things about conferences, as we all know, is the opportunity they afford to consolidate old friendships and make new contacts. Clusters of con-versation provide a more valuable way to spend ones time than attending sessions. At the last national meeting we escaped from the celebrations of the recent victory of Deep Blue over the dreaded Kasparov, to find just such a group, already engaged in an animated discussion ....

The system generates traffic flow measurements that enable traffic operations centers to monitor traffic movement and better respond to accidents Wal-Mart Stores (Bentonville, Ark.) Tektronix (Wilsonville, Ore.), a and congestion. This system, which manage its automated storage and models for its computer-assisted includes fuzzy logic and neural network retrieval system. The systems will Mexico), a producer of metals, has Calif.) is using visualization and digital monitor satellite signals in near real implemented an intelligent system to prototyping software for vehicle time, alerting operators to out-of-tolerance improve its zinc yield. The advanced design and manufacturing within its conditions and the presence of control expert system provides operator new concurrent engineering system. The application was developed and virtual manufacturing.

With a power screwdriver the computer, the web, robots, the Europe the Hindu-Arabic system of anyone can drive the hardest screw; automation of manufacturing will all numbers and the arithmetic algorithms with a calculator, anyone can get the conspire to separate the rich and they made possible. One of the numbers right; with an aircraft anyone quick from the poor and slow, hurrying first books after the Bible printed with can fly to Paris; and with Deep the trend to an informed, skilled, moveable type was an Arithmetic. Blue, anyone can beat the world chess and employed elite living among an Even so, the algorithms were not easy champion. Cognitive prostheses undermine uninformed, unskilled, and unemployed and not widely disseminated. But both history and 17th century tradesman could not by giving non-experts equivalent an understanding of human-machine multiply.

Autonomous agents are computer systems that are capable of independent action in dynamic, unpredictable environments. Agents are also one of the most important and exciting areas of research and development in computer science today. Agents are currently being applied in domains as diverse as computer games and interactive cinema, information retrieval and filtering, user interface design, and industrial process control. Agents '98 will build on the enormous success of the First International Conference on Autonomous Agents (Agents '97), held in Marina del Rey in February 1997. The conference welcomes submissions of original, high quality papers and videos with summaries concerning autonomous agents in a variety of embodiments and playing a variety of roles in their environments.

Artificial intelligence (AI) is the key technology in many of today's novel applications, ranging from banking systems that detect attempted credit card fraud, to telephone systems that understand speech, to software systems that notice when you're having problems and offer appropriate advice. These technologies would not exist today without the sustained federal support of fundamental AI research over the past three decades.

At a workshop held in Toulouse, France, in 1977, Gallaire, Minker, and Nicolas stated that logic and databases was a field in its own right. This was the first time that this designation was made. The impetus for it started approximately 20 years ago in 1976 when I visited Gallaire and Nicolas in Toulouse, France. In this article, I provide an assessment about what has been achieved in the 20 years since the field started as a distinct discipline. I review developments in the field, assess contributions, consider the status of implementations of deductive databases, and discuss future work needed in deductive databases.

Many AI researchers are today striving to build agent teams for complex, dynamic multi-agent domains, with intended applications in arenas such as education, training, entertainment, information integration, and collective robotics. Unfortunately, uncertainties in these complex, dynamic domains obstruct coherent teamwork. In particular, team members often encounter differing, incomplete, and possibly inconsistent views of their environment. Furthermore, team members can unexpectedly fail in fulfilling responsibilities or discover unexpected opportunities. Highly flexible coordination and communication is key in addressing such uncertainties. Simply fitting individual agents with precomputed coordination plans will not do, for their inflexibility can cause severe failures in teamwork, and their domain-specificity hinders reusability. Our central hypothesis is that the key to such flexibility and reusability is providing agents with general models of teamwork. Agents exploit such models to autonomously reason about coordination and communication, providing requisite flexibility. Furthermore, the models enable reuse across domains, both saving implementation effort and enforcing consistency. This article presents one general, implemented model of teamwork, called STEAM. The basic building block of teamwork in STEAM is joint intentions (Cohen & Levesque, 1991b); teamwork in STEAM is based on agents' building up a (partial) hierarchy of joint intentions (this hierarchy is seen to parallel Grosz & Kraus's partial SharedPlans, 1996). Furthermore, in STEAM, team members monitor the team's and individual members' performance, reorganizing the team as necessary. Finally, decision-theoretic communication selectivity in STEAM ensures reduction in communication overheads of teamwork, with appropriate sensitivity to the environmental conditions. This article describes STEAM's application in three different complex domains, and presents detailed empirical results.

This article describes FAQ FINDER, a natural language question-answering system that uses files of frequently asked questions as its knowledge base. Unlike AI question-answering systems that focus on the generation of new answers, FAQ FINDER retrieves existing ones found in frequently asked question files. Unlike information-retrieval approaches that rely on a purely lexical metric of similarity between query and document, FAQ FINDER uses a semantic knowledge base (WORDNET) to improve its ability to match question and answer. We include results from an evaluation of the system's performance and show that a combination of semantic and statistical techniques works better than any single approach.