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.

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.

Interestingly, the advantage for more effective access to traditional applications of agent of modeling others is diminished global and corporate information modeling, which requires an agent to when the volatility of the domain is repositories. These repositories are model the problem-solving processes low. Thus, the models of other agents increasingly multimedia, including of the interacting human to provide are more useful in variable domains, text, audio, graphics, imagery, and video. Now attention has turned appropriate feedback. Ole Mengshoel when they are a particularly valuable guide to predict what the other rational toward the problem of processing and D. C. Wilkins's (both of University agents are going to do. and managing multiple and heterogeneous of Illinois at Urbana-Champaign) media in a principled manner, presentation focused on recognizing

The Fifth International Conference on User Modeling (UM-96) is part of a recently established, biennial conference series that provides a forum for researchers in the field of user modeling and user-adapted interaction. The next major software revolution after graphic user interfaces will be software that adapts itself to the user. By adapting to the user's needs, preferences, knowledge, language, and even moods, software will attain new levels of usability and broad acceptance that would not be possible without built-in models of the user. This conference series provides a forum for recent research in the field, ranging from theoretical foundations to implemented systems to controlled studies of the human-computer interfaces of user-adapted systems.

Does machine learning really work? Yes. Over the past decade, machine learning has evolved from a field of laboratory demonstrations to a field of significant commercial value. Machine-learning algorithms have now learned to detect credit card fraud by mining data on past transactions, learned to steer vehicles driving autonomously on public highways at 70 miles an hour, and learned the reading interests of many individuals to assemble personally customized electronic newsAbstracts. A new computational theory of learning is beginning to shed light on fundamental issues, such as the trade-off among the number of training examples available, the number of hypotheses considered, and the likely accuracy of the learned hypothesis. Newer research is beginning to explore issues such as long-term learning of new representations, the integration of Bayesian inference and induction, and life-long cumulative learning. This article, based on the keynote talk presented at the Thirteenth National Conference on Artificial Intelligence, samples a number of recent accomplishments in machine learning and looks at where the field might be headed. [Copyright restrictions preclude electronic publication of this article.]

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.

Robotics Team 1 from Kansas State University was the team that perfectly completed the Office Navigation event in the shortest time at the fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence. The team, consisting of Michael Novak and Darrel Fossett, developed its code in an undergraduate software-engineering course. Its C code used multiple threads to provide separate autonomous agents to solve the meeting scheduling task, control the sonar sensors, and control the actual robot motion. The team's robot software was nicknamed SLICK WILLIE for the way it gracefully moved through doorways and around obstacles.

Ballots will be due Applications of Artificial Intelligence have an accepted technical paper, back at the AAAI office no later than (IAAI-97) will be held in and then to students who are actively June 13. Conference on Knowledge Discovery are encouraged to apply. For further information be held November 8-10 at the Massachusetts following the American Statistical about the Scholarship Program, Institute of Technology in Association annual meeting in Anaheim. The topics Information about these conferences please contact AAAI at scholarships@aaai.org, of seven symposia will be: is available by writing to All student scholarship recipients Context in Knowledge Representation Registration materials for AAAI-97, will be required to participate in the and Natural Language Sasa IAAI-97, and KDD-97 are now available Student Volunteer Program to support Buvac (buvac@cs.stanford.edu), For further information, participation is a valuable contribution.

The workshop entitled "Gaps and Bridges: New Directions in Planning and Natural Language Generation" was held on 12 August 1996 in Budapest, Hungary. This article describes the four sessions of the workshop and summarizes the important themes that were revealed.

The YODA Robot Project at the University of Southern California/Information Sciences Institute consists of a group of young researchers who share a passion for autonomous systems that can bootstrap its knowledge from real environments by exploration, experimentation, learning, and discovery. Our goal is to create a mobile agent that can autonomously learn from its environment based on its own actions, percepts, and mis-sions. Our participation in the Fifth Annual AAAI Mobile Robot Competition and Exhibition, held as part of the Thirteenth National Conference on Artificial Intelligence, served as the first milestone in advancing us toward this goal. YODA's software architecture is a hierarchy of abstraction layers, ranging from a set of behaviors at the bottom layer to a dynamic, mission-oriented planner at the top. The planner uses a map of the environment to determine a sequence of goals to be accomplished by the robot and delegates the detailed executions to the set of behaviors at the lower layer. This abstraction architecture has proven robust in dynamic and noisy environments, as shown by YODA's performance at the robot competition.