Abstract: It has been claimed that production systems have several advantages over other representational schemes. These include the potential for general self-augmentation (i.e., learning of new behavior) and the ability to function in complex environments. The production system language, OPS, was implemented to test these claims. In this paper we explore some of the issues that bear on the design of production system languages and try to show the adequacy of OPS for its intended purpose. I. INTRODUCTION Much of the work that has been done with production systems during the past few years has had as its primary goal the development of systems that are expert in some particular task. The tasks so far addressed include: chemical inference [Buchanan and Lederberg, J 971], medical diagnosis [Davis, Buchanan, and Shortliffe, 1975], discovery in mathematics [Lenat, 1976], speech recognition [Erman and Lesser, 1975; McCracken, 1977], and automatic programming [Barstow, 1977]. Although many of these systems have shown impressive power in the particular task for which they were designed, there remains a question of how suitable the production system representation is for large general problem solving programs. The Instructable Production System (IPS) project at CMU [Rychener and Newell, 1977] is attempting to answer this question. It has been claimed that production systems are capable of learning in a nontrivial way. If this is true, a production system should be able to learn not only facts, but also new behaviors.

Hierarchies are observed in the levels of description used in image understanding along a few dimensions: processing unit, detail, composition and scene/view distinction. Emphasis is placed on the importance of explicitly handling the hierarchies both in representing knowledge and in using it. A scheme of "knowledge block" representation which is structured along the processing-unit hierarchy is also presented. I. INTRODUCTION Image Understanding System(IUS) constructs a description of the scene being viewed from an array of image sensory data: intensity, color, and sometimes range data. Image understanding is best characterized by description, whereas pattern recognition by classification, and image processing by image output.

Traditional scheduling algorithms (using the techniques of PERT charts, decision analysis or operations rrsrarrh) require well-defined, quantitative, complete sets of constrainls*. They are insufficient for scheduling situations where the problem description is ill-defined, involving incomplete, possibly inconsistent and generally qualitative constraints. The NUDGE program uses an extensive knowledge base to debug scheduling requests by supplying typical values for qualitative constraints, supplying missing details and resolving minor inconsistencies. The result is that an informal request is converted to a complete description suitable for a traditional scheduler. To implement the NUDGE program, a knowledge representation language -- FRL-0 -- based on a few powerful generalizations of the traditional property list representation has been developed.

U.S.A. Abstract: This paper presents a discussion of means of describing the discourse and its components which makes speech act interpretation and anaphora disambiguation possible with minimal search of the knowledge in the database. A portion of this paper will consider how a frames representation of sentences and common sense knowledge provides a mechanism for representing the postulated discourse components. Finally some discussion of the use of the discourse model and of frames in a discourse understanding program for a personal assistant will be presented. Introduction The person who communicates with a personal assistant, whether human or machine, wants to request some action of the assistant via sentences in English. Generally, a single sentence is insufficient to capture all the information that is to be given as well as an unnatural way to make a request. However, as several example dialogues below will show, the human user does not tightly relate the sentences s/he speaks about a particular subject. It is instead the job of the hearer to interpret how the incoming sentence is related to the previous discourse. Each sentence or clause of a discourse that makes some demand upon the hearer must be interpreted for the kind of demand being made. These demands are generally referred to as speech acts {1}. However, the speech acts are not just strings of individual requests. They have a connecting pattern which the hearer must extract as the discourse goes on. Thus the hearer's task is twofold: to interpret the speech act in a clause and to relate that speech act to the overall discourse. In this paper this two-pronged task will be referred to as speech act interpretation. Closely associated with speech act interpretation is the process of understanding what the various noun and pronoun phrases of the incoming sentence refer to. Speakers denote previously mentioned objects in a variety of ways with apparent ambiguity in the choice of referents.

This view is nominalism, and leads to a quite different sort of semantic intuition, in which, for example, red denotes not a property of physical individuals, but the (rather disconnected) individual consisting of all pieces of red stuff in the world. Other similar confusions are also made. For example, logic is no worse (and no better) than Conceptual Dependency at representing warm, human facts about people hitting each other, (4) Logic doesn't give "the ultimate in decomposition of knowledge". Winograd, in his widely cited discussion [23] of the assertional/procedural controversy, draws a distinction between logic's atomistic view of knowledge, in which a representation is seen as a set of separate disconnected facts, and the proceduralist's holistic view in which interactions between procedures have prominence. But this is exactly the opposite of the truth.

In English, "1ikewise", "too", "similarly", etc., followed optionally by a preposition and then by a noun phrase indicate that the role filled by the new noun phrase in a previously mentioned predicate is a non-subject one.

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Amsterdam: North-Holland

In L. Steels (Ed.), Advances in natural language processing. Antwerp, Belgium: University of Antwerp.

San Francisco: Freeman