Automatic speech recognition is one of the fastest growing and commercially most promising applications of natural language technology. The technology has achieved a point where carefully designed systems for suitably constrained applications are a reality. Commercial systems are available today for such tasks as large-vocabulary dictation and voice control of medical equipment. This article reviews how state-of-the-art speech-recognition systems combine statistical modeling, linguistic knowledge, and machine learning to achieve their performance and points out some of the research issues in the field.

In recent years, there has been a flurry of research into empirical, corpus-based learning approaches to natural language processing (NLP). The success of these approaches has stimulated research in using empirical learning techniques in other facets of NLP, including semantic analysis -- uncovering the meaning of an utterance. This article is an introduction to some of the emerging research in the application of corpus-based learning techniques to problems in semantic interpretation. In particular, we focus on two important problems in semantic interpretation, namely, word-sense disambiguation and semantic parsing.

The $100,004 Fredkin Prize for Computer Chess, created in 1980 to honor the first program to beat a reigning world chess champion, was awarded to the inventors of the Deep Blue chess machine Tuesday, July 29, at the annual meeting of the Association for the Advancement of Artificial Intelligence (AAAI) in Providence, Rhode Island.

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.

Machine translation of human languages (for example, Japanese, English, Spanish) was one of the earliest goals of computer science research, and it remains an elusive one. Like many AI tasks, trans-lation requires an immense amount of knowledge about language and the world. Recent approaches to machine translation frequently make use of text-based learning algorithms to fully or partially automate the acquisition of knowledge. This article illustrates these approaches.

Automatic speech recognition is one of the fastest growing and commercially most promising applications of natural language technology. The technology has achieved a point where carefully designed systems for suitably constrained applications are a reality. Commercial systems are available today for such tasks as large-vocabulary dictation and voice control of medical equipment. This article reviews how state-of-the-art speech-recognition systems combine statistical modeling, linguistic knowledge, and machine learning to achieve their performance and points out some of the research issues in the field.

In adjacent agents from dropping their commitments; (held in Kyoto, Japan, on 10-13 December domains (logical philosophy, social or better, how to regulate 1996). Both the program committee philosophy, decision theory), both legal agents dropping their commitments and the contributors included and social norms have received to a joint action to not disrupt the scientists from different backgrounds considerable, if not satisfactory, attention. The discussion addressed on, has contributed dramatically to These tasks have now entered the several issues: (1) What is the the attention given by the scientific MAS field's common knowledge. Often action is reduced to decision authorization, access regulation, For example, the existence of so-called (that is, a choice among one's privacy maintenance, respect of decency, Georgeff 1991) have shown that we and Tennenholtz 1992). Why? Don't we need a reciprocity.

This historical, developmental approach to AI was visible throughout the conference. The presented at the 1997 Mobile Robot the end of the twentieth century," strong tutorial sessions, workshops, Competition, for example, time was "A computer beat natural language, machine learning to Robot Competition get better, and the the world champion at chess. A robot reasoning and representation, the events get harder. This year, the events is on Mars making a few of its own decisions." More than 20 teams competed AI's visible achievements, noted Allen deftly compared AI to the stages this year. At its heart, it is a technical of human development.

New additions in 1998 (UAI-98), July 24-26, 1998 February 1, 1998, use of the new 650 will be the Integrated Systems Track of --papers due February 23, 1998 code will be mandatory; so, please update the technical program and the Intelligent --www.uai98.cbmi.upmc.edu The Computing Research Association All regular members in good February 27: Intelligent Systems is planning a Career Development standing are encouraged to consider Demonstration proposals due. Details can be found at www. (at least one from a current is available by writing to ncai@ cra.org. AAAI fellow) must accompany nominations. Nomination materials are also available on AAAI is delighted to announce the AAAI-98 Workshop Program.

This article posits the idea of robot learning as a new subfield. The results of the Robolearn-96 Workshop provide evidence that learning in modern robotics is distinct from traditional machine learning. The article examines the role of robotics in the social and natural sciences and the potential impact of learning on robotics, generating both a continuum of research issues and a description of the divergent terminology, target domains, and standards of proof associated with robot learning. The article argues that although robot learning is a new subfield, there is significant potential for synergy with traditional machine learning if the differences in research cultures can be overcome.