THE REPRESENTATION AND USE OF FOCUS IN A SYSTEM FOR UNDERSTANDING DIALOGS Barbara J. Grosz Artificial Intelligence Center SRI International, Menlo Park, California 94025 ABSTRACT As a dialog progresses the objects and actions that are most relevant to the conversation, and hence in the focus of attention of the dialog participants, change. This paper describes a representation of focus for language understanding systems, emphasizing its use in understanding taskoriented dialogs. The representation highlights that part of the knowledge base relevant at a given point in a dialog. A model of the task is used both to structure the focus representation and to provide an index into potentially relevant concepts in the knowledge base The use of the focus representation to make retrieval of items from the knowledge base more efficient is described. I INTRODUCTION To understand the sentences in a discourse, a computer system, like a person, must have knowledge about the domain of the discourse. However, the knowledge required to understand even simple, reallife domains is so extensive that it will overwhelm a system that does not apply it selectively. This means that the ability to focus on the subset of knowledge relevant to a particular situation is crucial. This paper addresses the problem of focus from the perspective of building a computer system that can participate in a task-oriented dialog. A representation for focus is presented; its use is illustrated by showing how the referents of definite noun phrases are identified. A combination of contextual factors influences the interpretation of an utterance. In fact, what is usually meant by "the context of an utterance" is precisely that set of constraints which together direct attention to the concepts of interest in the discourse in which the utterance occurs. Both the preceding discourse context - - the utterances that have already occurred -- and the situational context -- the environment in which an utterance occurs -- affect the interpretation of the utterance. For a dialog, the situational context includes the physical environment, the social setting, and the relationship between the participants in the dialog. This paper shows how the task and dialog contexts combine to provide a focus on those concepts relevant to the interpretation of utterances in task-oriented dialogs.

This paper discusses an effort in the application of artificial intelligence to the access of data from a large, distributed data base over a computer network. A running system is described that provides real-time access over the ARPANET to a data base distributed over several machines. The system accepts a rather wide range of natural language questions about the data, plans a sequence of appropriate queries to the data base management system to answer the question, determines on which machine(s) to carry out the queries, establishes links to those machines over the ARPANET, monitors the prosecution of the queries and recovers from certain errors in execution, and prepares a relevant answer. In addition to the components that make up the demonstration system, more sophisticated functionally equivalent components are discussed and proposed. The work described in this paper represents the joint efforts of an integrated, energetic group at SRI. Members of this group include Rich Fikes (now at Xerox PARC), Koichi Furukawa (now at ETL).

See also: Stanford Heuristic Programming Project Memo HPP-77-25Proc. IJCAI-77: Fifth International Joint Conference on Artificial Intelligence, pp 1014-1029

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This text is a description of a computer-based system designed to assist physicians with clinical decision-making. This system, termed MYCIN, utilizes computer techniques derived principally from the subfield of computer science known as artificial intelligence (AI). MYCIN's task is to assist with the decisions involved in the selection of appropriate therapy for patients with infections.

MYCIN contains considerable medical expertise and is also a novel application of computing technology. Thus, this text is addressed both to members of the medical community, who may have limited computer science backgrounds, and to computer scientists with limited knowledge of medical computing and clinical medicine. Some sections of the text may be of greater interest to one community than to the other. A guide to the text follows so that you may select those portions most pertinent to your particular interests and background.

The complete book in a single file.

In researc which lan uac; assumed knowled way it use of provide impreci recent h into a is to e. In that on re of th is used the cons s, to na se acous years, utomati (r,et a nost e need e lan u (pragma traints ke sens tic sit there has c speech u computer of this s to pro are (its s tics). It and expec e of the i nal that i been a nderstan to und recent a vide th yntax an will th tations nherentl s human rroat increase in dine, the purpose of erstand the spoken ctivity, it has been e computer with a d semantics) and the en be able to make which this knowledfre y vaf ue, sloppy and soeech. Syntactic constraints and expectations are based on the patterns formed by a Riven set of linguistic objects, e. .

At the present stage of research in artificial intelligence , machines are stil l remote from achieving a level of intelligence comparable in complexity to human thought. As computer applications become more sophisticated, however, and thus more influential in human affairs , it becomes increasingly important to understand both the capabilities and limitations of machine Intelligence and its potential impact on society. To this end, the artificial intelligence field was ex­amined in a systematic manner. The study was divided into two parts : (1) Delineation of areas of artificial intelligence, and postulatio " of hypothetical prod­ucts resulting from progress in the field , and (2) A judgmental portion, which involved appli­cations and implications of the products to society . For the latter purpose, a Delphi study was conducted among experts in the artificial intelligence field to solicit their opinion concerning prototype and com­mercial dates for the products, and the possibility and desirability of their applications and implications .In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.

This paper considers various factors affecting system organization for speech understanding research. The structure of the Hearsay system based on a set of cooperating, independent processes using the hypothesize-and-test paradigm is presented. Design considerations for the effective use of multiprocessor and network architectures in speech understanding systems are presented: control of processes, interprocess communication and data sharing, resource allocation, and debugging are discussed.See also: IEEE Xplore.In IJCAI-73: THIRD INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 20-23 August 1973, Stanford University Stanford, California.