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Showing papers on "Knowledge representation and reasoning published in 1983"


Journal ArticleDOI
TL;DR: In this article* the more common interpretations of IS-A are cataloged and some differences between systems that, on the surface, appear very similar are pointed out.
Abstract: A was quite simple. Today, however, there are almost as many meanings for this inheritance link as there are knowledge-representation systems. Many systems for representing knowledge can be conisidered semantic networks largely becau.se they feature the notion of an explicit taxonomic hierarchy, a tree or lattice-like structure for categorizing classes of things in the world being represented. The backbone of the hierarchy is proFvided by some sort of \"inheritance\" link betweeni the representational objects, known as \"nodes\" in some systems and as \"frames\" in others. This link, otten called \"IS-A\" (also known as \"IS,\" \"SUPERC,\" \"AKO,\" \"SUBSET,\" etc.), has been perhaps the most stable element of semantic nets as they have evolved over t he yvears. Unfortunately, thi.s stability may be illusory. There are almost as many meanings for the IS-A link as there are knowledge-representation systems. In this article* we catalog the nmore common interpretations of IS-A and point out some differences between systems that, on the surface, appear very similar. Background. The idea of IS-A is quite simple. Early in the history of semantic nets, researchers observed that much representation of the world was concerned with the conceptual relations expressed in English sentences such as \"John is a bachelor\" and \"A dog is a domesticated carnivorous mammal.\" That is, two predominant forms of statements handled by Al knowledge-representation systems were the predication, expressing that an individual (e.g., John) was of a certain type (e.g.,

592 citations


Journal ArticleDOI
TL;DR: The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it, called Krypton, which clearly distinguishes between definitional and factual information.
Abstract: A great deal of effort has focused on developing frame-based languages for knowledge representation. While the basic ideas of frame systems are straightforward, complications arise in their design and use. The authors have developed a design strategy for avoiding these types of problems and have implemented a representation system based on it. The system, called Krypton, clearly distinguishes between definitional and factual information. In particular, Krypton has two representation languages, one for forming descriptive terms and one for making statements about the world using these terms. Further, Krypton provides a functional view of a knowledge base, characterized in terms of what it can be asked or told, rather than in terms of the particular structures it uses to represent knowledge. 11 references.

383 citations


Proceedings ArticleDOI
Karen Kukich1
15 Jun 1983
TL;DR: Three fundamental principles of the technique are the use of domain-specific semantic and linguistic knowledge, its use of macro-level semantic and language constructs, and its production system approach to knowledge representation.
Abstract: Knowledge-Based Report Generation is a technique for automatically generating natural language reports from computer databases. It is so named because it applies knowledge-based expert systems software to the problem of text generation. The first application of the technique, a system for generating natural language stock reports from a daily stock quotes database, is partially implemented. Three fundamental principles of the technique are its use of domain-specific semantic and linguistic knowledge, its use of macro-level semantic and linguistic constructs (such as whole messages, a phrasal lexicon, and a sentence-combining grammar), and its production system approach to knowledge representation.

245 citations


Journal ArticleDOI
TL;DR: The approach to the representation of commonsense knowledge described in this article is based on the idea that propositions characterizing commomsense knowledge are for the most part, dispositions -- that is, propositions with implied fuzzy quantifiers.
Abstract: The approach to the representation of commonsense knowledge described in this article is based on the idea that propositions characterizing commomsense knowledge are for the most part, dispositions -- that is, propositions with implied fuzzy quantifiers. To deal with dispositions systematically the author uses Fuzzy-Logic -- The logic underlying approximate or fuzzy reasoning

165 citations


Proceedings Article
08 Aug 1983
TL;DR: This work discusses issues and demonstrate some uses of the classification algorithm, which takes a new Concept and determines other Concepts that either subsume it or that it subsumes, thereby determining the location for the new Concept within a given taxonomy.
Abstract: KL-ONE lets one define and use a class of descriptive terms called Concepts, where each Concept denotes a set of objects A subsumption relation between Concepts is defined which is related to set inclusion by way of a semantics for Concepts. This subsumption relation defines a partial order on Concepts, and KL-ONE organizes all Concepts into a taxonomy that reflects this partial order. Classification is a process that takes a new Concept and determines other Concepts that either subsume it or that it subsumes, thereby determining the location for the new Concept within a given taxonomy. We discuss these issues and demonstrate some uses of the classification algorithm.

163 citations


Proceedings Article
19 Sep 1983
TL;DR: This is a brief overview of terminology and issues related to Knowledge Representation (here-after KR) research, intended primarily for researchers working on Semantic Data Models within Database Management and Program Specifications within Programming Languages/Software Engineering.
Abstract: This is a brief overview of terminology and issues related to Knowledge Representation (here-after KR) research, intended primarily for researchers working on Semantic Data Models within Database Management and Program Specifications within Programming Languages/Software Engineering. Knowledge Representation is a central problem in Artificial Intelligence (AI) today. Its importance stems from the fact that the current design paradigm for “intelligent” systems stresses the need for expert knowledge in the system along with associated knowledge-handling facilities. This paradigm is in sharp contrast to earlier ones which might be termed “power-oriented” [Goldstein and Papert 77] in that they placed an emphasis on general purpose heuristic search techniques [Nilsson 71].

117 citations


Book ChapterDOI
TL;DR: The chapter provides an overview, from a theoretical viewpoint, of the conceptual structure methodology and describes the functioning of the systems that have been developing to give concreteness to the theoretical ideas.
Abstract: Publisher Summary This chapter describes an approach to the design of medical decision-making systems based on the notion of conceptual structures for knowledge representation. The chapter provides an overview, from a theoretical viewpoint, of the conceptual structure methodology and describes the functioning of the systems that have been developing to give concreteness to the theoretical ideas. The central system in this group of systems is called MDX, which is a diagnostic system, that is, it attempts to classify a given case as an element of a disease taxonomy. This system interacts with two other systems during its problem solving, PATREC and RADEX, the former a knowledge-based patient database system that answers MDX's queries about patient data, and the latter a radiological consultant which helps MDX in the interpretation of various kinds of imaging data. Both PATREC and RADEX are invoked by MDX as needed, but MDX is in control of the overall diagnostic process. The chapter discusses the inadequacies of medical reasoning approaches based on Bayesian approaches.

104 citations


Journal ArticleDOI
TL;DR: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations.
Abstract: The author discusses a number of issues that serve as research goals for discovering the principles of knowledge representation, using techniques and concepts evolved while developing the knowledge-representation system KL-one as illustrations. The focus is on what constitutes a good representational system and a good set of representational primitives for dealing with an open-ended range of knowledge domains. Issues of interest include those problems that arise in attempting to construct intelligent computer programs that use knowledge to perform some task. 7 references.

85 citations


Journal ArticleDOI
TL;DR: Methods are described that are designed to supplement a deductive question-answering algorithm that is now operational that draws on a base of logical propositions organized as a semantic net.
Abstract: The development of a simple question-answering system is considered. In particular, methods are described that are designed to supplement a deductive question-answering algorithm that is now operational. The algorithm draws on a base of logical propositions organized as a semantic net. The net permits selective access to the contents of individual mental worlds and narratives, to sets of entities of any specified type, and to propositions involving any specified entity and classified under any specified topic. The problems involved in determining type, part-of, color, and time relationships are discussed. It is shown that much combinatory reasoning in a question-answering system can be short-circuited by the use of special graphical and geometrical methods. 13 references.

85 citations


Journal ArticleDOI
TL;DR: Current scene analysis methodology is examined under two criteria: descriptive adequacy, the ability of a representational formalism to capture the essential visual properties of objects and the relationships among objects in the visual world, and procedural adequacy), the capability of the representation to support efficient processes of recognition and search.
Abstract: The central issue in artificial intelligence the representation and use of knowledge unifies areas as diverse as natural-language understanding, speech recognition, story understanding, planning, problem solving, and vision. This article focuses on how computational vision systems represent knowledge of the visual world. It examines current methodology under two criteria: descriptive adequacy, the ability of a representational formalism to capture the essential visual properties of objects and the relationships among objects in the visual world, and procedural adequacy, the capability of the representation to support efficient processes of recognition and search. A major theme in computational vision has been the distinction between the methodology of image analysis (or early vision) and scene analysis (or high-level vision). Briefly, image analysis can be characterized as the science of extracting from images useful descriptions of lines, regions, edges, and Ssurface characteristics up to the level of Marr's 21/2 -D sketch. It is generally assumed that image analysis is domain independent and passive, that is, data driven. Scene analysis attempts to recognize visual objects and their configurations. It is viewed as domain dependent and goal driven, mnotivated by the necessity of identifying particular objects expected to be present in a scene. Although some may disagree, these distinctions should be seen not as a strict dichotomy but as a spectrum. Early vision exploits constraint.s that are usually valid in the particular visual world for which it has evolved (or been designed). Although early visioni is predominantly data driven, high-level visual processes must be able to establish parameters for and control the attention of lower level processes. As we argue later, efficient scene analysis systems must combinie goal-driven and datadriven recognition processes. If that dichotomy is actually a spectrum then establishing the exact boundary is not a research issue. In this article, we outline current scene analysis methodology (early vision is ably described elsewherelt2) and identity a number of its deficiencies. In response to these problems, some recent systems use schema-based knowledge representations. Examples taken from one called Mapsee2 illustrate our arguments.

83 citations


Journal ArticleDOI
TL;DR: A knowledge-embedding language called OMEGA is used to embed knowledge of the organization into an office worker's work station in order to support the office worker in problem solving and uses OMEGA's viewpoint mechanism, which is a general contradiction-handling facility.
Abstract: Permission to copy without fee all or part of this material is granted provided that the copies are not made or distributed for direct commercial advantage, the ACM copyright notice and the title of the publication and its date appear, and notice is given that copying is by permission of the Association for Computing Machinery. To copy otherwise, or to republish, requires a fee and/or specific permission. An approach to supporting work in the office is described. Using and extending ideas from the field of artificial intelligence (AI) we describe office work as a problem-solving activity. A knowledge-embedding language called OMEGA is used to embed knowledge of the organization into an office worker's work station in order to support the office worker in problem solving. A particular approach to reasoning about change and contradiction is discussed. This approach uses OMEGA's viewpoint mechanism, which is a general contradiction-handling facility. Unlike other knowledge representation systems, when a contradiction is reached the reasons for the contradiction can be analyzed by the deduction mechanism without having to resort to search mechanisms such as a backtracking. The viewpoint mechanism is the heart of the problem-solving support paradigm, a paradigm which supplements the classical AI view of problem solving. An example is presented in which OMEGA's facilities are used to support an office worker's problem-solving activities. The example illustrates the use of viewpoints and of OMEGA's capabilities to reason about its own reasoning processes. Categories and Subject Descriptors: H.3.4 [ Information Storage and Retrieval ]: Systems and Software— information networks ; H.4.1 [ Information Systems Applications ]: Office Automation; I.2.1 [ Artificial Intelligence ]: Applications and Expert Systems— office automation ; I.2.4 [ Artificial Intelligence ]: Knowledge Representation Formalisms and Methods— semantic networks

Journal ArticleDOI
TL;DR: A taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas is presented.
Abstract: Machine learning has always been an integral part of artificial intelligence, and its methodology has evolved in concert with the major concerns of the field. In response to the difficulties of encoding ever-increasing volumes of knowledge in modern AI systems, many researchers have recently turned their attention to machine learning as a means to overcome the knowledge acquisition bottleneck. This article presents a taxonomic analysis of machine learning organized primarily by learning strategies and secondarily by knowledge representation and application areas. A historical survey outlining the development of various approaches to machine learning is presented from early neural networks to present knowledge-intensive techniques.

Proceedings Article
08 Aug 1983
TL;DR: This paper demonstrates a methodology for collecting and analysing observations of experts at work, in order to find the conceptual framework used for the particular domain, and develops a representation for qualitative knowledge of the structure and behavior of a mechanism.
Abstract: The ability to identify and represent the knowledge that a human expert has about a particular domain is a key method in the creation of expert computer system. The first part of this paper demonstrates a methodology for collecting and analysing observations of experts at work, in order to find the conceptual framework used for the particular domain. The second part develops a representation for qualitative knowledge of the structure and behavior of a mechanism. The qualitative simulation, or envisionment, process is given a qualitative structural description of a mechanism and some initialization information, and produces a detailed description of the mechanism's behavior. This "vertical" slice of the construction of a cognitive model demonstrator, an effective knowledge acquisition method for the purpose of determining the structure of the representation itself, not simply the content of the knowledge to be encoded in that representation. Most importantly, it demonstrates the interaction among constraints derived from the textbook knowledge of the domain, from observations of the human expert, and from the computational requirements of successful performance.

Proceedings Article
08 Aug 1983
TL;DR: Three aspects of learning to program are described-the organization and compilation of problem-solving operators, the impact of knowledge representation, and the impactof working memory limitations.
Abstract: Three aspects of learning to program are described-the organization and compilation of problem-solving operators, the impact of knowledge representation, and the impact of working memory limitations. The GRAPES system simulates the organization and compilation of these operators. The simulation of one problem solving episode is discussed. Also discussed are the impact of different data notations and the impact of working memory load on successful application of LISP functions.

Proceedings Article
22 Aug 1983
TL;DR: This work has attempted to overcome limitations in a new, hybrid knowledge representation system, called "KRYPTON", which has two representation languages, a frame-based one for forming domain-specific descriptive terms and a logic-basedOne for making statements about the world.
Abstract: The demands placed on a knowledge representation scheme by a knowledge-based system are generally not all met by any of today's candidates. Representation languages based on frames or semantic networks have intuitive appeal for forming descriptions but tend to have severely limited assertional power, and are often fraught with ambiguous readings. Those based on first-order logic are less limited assertionally, but are restricted to primitive, unrelated terms. We have attempted to overcome these limitations in a new, hybrid knowledge representation system, called "KRYPTON". KRYPTON has two representation languages, a frame-based one for forming domain-specific descriptive terms and a logic-based one for making statements about the world. We here summarize the two languages, a functional interface to the system, and an implementation in terms of a taxonomy of frames and its interaction with a first-order theorem prover.

Proceedings ArticleDOI
22 Aug 1983
TL;DR: An overview of RESEARCHER, a computer system being developed at Columbia that reads natural language text in the form of patent abstracts and creates a permanent long-term memory based on concepts generalized from these texts, forming an intelligent information system.
Abstract: Described in this paper is a computer system, RESEARCHER, being developed at Columbia that reads natural language text in the form of patent abstracts and creates a permanent long-term memory based on concepts generalized from these texts, forming an intelligent information system. This paper is intended to give an overview of RESEARCHER. We will describe briefly the four main areas dealt with in the design of RESEARCHER: 1) knowledge representation, where a canonical scheme for representing physical objects has been developed, 2) memory-based text processing, 3) generalization and generalization-based memory organization that treats concept formation as an integral part of understanding, and 4) generalization-based question answering.

Journal ArticleDOI
Israel1
TL;DR: Theoreticians have reached no consensus on how to solve the Al problem, but there are two opposing philosophical viewpoints and a flurry of research activity along these two directions.
Abstract: Theoreticians . .. have reached no consensus on how to solve the Al problem-on how to make true thinking machines. Instead, there are two opposing philosophical viewpoints and a flurry of research activity along these two directions. The different viewpoints were represented at a recent meeting of the American Association for Artificial Intelligence by Marvin Minsky and John McCarthy.... McCarthy believes that the way to solve the Al problem is to design computer programs to reason according to the well-worked-out languages of mathematical logic, whether or not that is actually the way people think. Minsky believes that a fruitful approach is to try to get computers to imitate the way the human mind works, which he thinks, is almost certainly not with mathematical logic.

Journal ArticleDOI
TL;DR: This special issue introduces the importance, diversity, and vigor of knowledge representation as a research activity to a wider audience by mapping out the basic approaches to knowledge representation that have developed over the years.
Abstract: In contrast to conventional database systems, AI systems require a knowledge base with diverse kinds of knowledge. These include, but are not limited to, knowledge about objects, knowledge about processes, and hard-to-represent commonsense knowledge about goals, motivation, causality, time, actions, etc. Attempts to represent this breadth of knowledge raise many questions: (1) How do we structure the explicit knowledge in a knowledge base? (2) How do we encode rules for manipulating a knowledge base's explicit knowledge to infer knowledge contained implicitly within the knowledge base? (3) When do we undertake and how do we control such inferences? (4) How do we formally specify the semantics of a knowledge base? (5) How do we deal with incomplete knowledge? (6) How do we extract the knowledge of an expert to initially "stock" the knowledge base? (7) How do we automatically acquire new knowledge as time goes on so that the knowledge base can be kept current? This special issue introduces this important area of artificial intelligence to a wider audience. The core of the 15 articles, contributed by a broad spectrum of researchers on various aspects of knowledge representation, show the importance, diversity, and vigor of knowledge representation as a research activity. This introduction provides some background and context to these articles by mapping out the basic approaches to knowledge representation that have developed over the years.

Proceedings Article
22 Aug 1983
TL;DR: Theory resolution constitutes a set of complete procedures for building nonequational theories into a resolution theorem-proving program so that axioms of the theory need never be resolved upon.
Abstract: Theory resolution constitutes a set of complete procedures for building nonequational theories into a resolution theorem-proving program so that axioms of the theory need never be resolved upon. Total theory resolution uses a decision procedure that is capable of determining inconsistency of any set of clauses using predicates in the theory. Partial theory resolution employs a weaker decision procedure that can determine potential inconsistency of a pair of literals. Applications include the building in of both mathematical and special decision procedures, such as for the taxonomic information furnished by a knowledge representation system.

Journal ArticleDOI
TL;DR: The general architecture and fundamental design criteria of a system presently being developed at the University of Udine, aimed at allowing non-technical users to directly access through natural language the services offered by online databases, are presented.

Journal ArticleDOI
TL;DR: PSN is one attempt that focuses on the integration of semantic network and procedural notions in knowledge-representation languages.
Abstract: Knowledge-representation languages have been classified traditionally as declarative or procedural, depending on whether their basic features come from mathematical logic or data structures on one hand, or from programming languages on the other hand. Procedural representation languages are particularly well suited for heuristic knowledge, and their use can lead to efficient searching on the part of an expert system. Many attempts have been made to integrate features of declarative and procedural representation languages. PSN is one attempt that focuses on the integration of semantic network and procedural notions

Journal ArticleDOI
TL;DR: Information systems, while contributing to organizational efficiency, may at the same time constrain organizational adaptability and the high cost of system modification is related to a broader problem: the transportability of knowledge as represented in computer programs.

Proceedings Article
08 Aug 1983
TL;DR: A formal definition of a frame data model is presented in terms of a denotational semantics approach using a subset of META-IV, which ultimately lead to the formulation of a set of operations in the frameData model.
Abstract: Standard knowledge representation languages are seriously lacking an explicit formal semantic specification. This may cause considerable trouble when applied to large amounts of rapidly changing data. Based on an abstract data type view of knowledge representation languages a formal definition of a frame data model is presented in terms of a denotational semantics approach using a subset of META-IV. After introducing some basic concepts of the model several semantic integrity constraints are outlined which ultimately lead to the formulation of a set of operations in the frame data model.

Proceedings Article
08 Aug 1983
TL;DR: An extension of FRL (Frame Representation Language) which supports the encoding of reasoning knowledge within a frame-based formalism is described, called HPRL (Heuristic Programming and Representation language).
Abstract: This paper describes an extension of FRL (Frame Representation Language) which supports the encoding of reasoning knowledge within a frame-based formalism. The extension is called HPRL (Heuristic Programming and Representation Language). The declarative representation of reasoning knowledge in the same formalism that is used to represent domain knowledge results in a powerful tool for the construction of expert systems. Reasoning knowledge is easy to describe, examine and modify. Rules can be reflexive, allowing the construction of powerful meta-rules. HPRL runs on a Vax 11/780, and on the HP-9836. It has been used for various exploratory projects at Hewlett-Packard, including a program to diagnose faults during IC manufacturing, a program for analyzing dual-channel ECG information to diagnose arrhythmias, and a program for analyzing spectra from infrared and mass spectrometers.

Proceedings Article
08 Aug 1983
TL;DR: The CAA system inherits its basic control mechanisms from the ALVEN system, such as the change/focus attention mechanism with similarity links and the hypothesis rating mechanism, and uses causal links extensively to represent various causal and temporal relations between concepts in the physiological event domain.
Abstract: An expert system, Causal Arrhythmia Analyzer (CAA), is being developed to establish a framework for the recognition of time varying signals of a complex repetitive nature, such as electrocardiograms (ECGs). Using a stratified knowledge base the CAA system discerns several perspectives about the phenomena of underlying entities, such as the physiological event knowledge of the cardiac conduction system and the morphological waveform knowledge of ECG tracings, where conduction events are projected into the observable waveform domain. Projection links have been defined to represent projection in CAA's frame-based formalism and are used to raise hypotheses across different KBs. The CAA system also introduces and uses causal links extensively to represent various causal and temporal relations between concepts in the physiological event domain. Its control structure uses causal links to predict unseen events from recognized events, to confirm these event hypotheses against input data, and to calculate the degree of integrity among causally related events. The meta-knowledge representation of statistical information about events facilitates a default reasoning mechanism and supports this expectation process providing context sensitive statistical information. The CAA system inherits its basic control mechanisms from the ALVEN (A Left VENtricular Wall Motion Analysis) system [Tsotsos 1981], such as the change/focus attention mechanism with similarity links and the hypothesis rating mechanism. A prototype CAA system with a limited number of abnormalities has been implemented using the knowledge representation language PSN (Procedural Semantic Networks) [Levesque & Mylopoulos 1979]. The prototype has so far demonstrated satisfactory results using independently sampled ECG data.

Proceedings Article
John Kunz1
22 Aug 1983
TL;DR: The objective of this research is to demonstrate a methodology for the design and use of a physiological model in a computer program that suggests medical decisions and is named AI/MM.
Abstract: The objective of this research is to demonstrate a methodology for the design and use of a physiological model in a computer program that suggests medical decisions. The physiological model is based on first principles and facts of physiology and anatomy, and it includes inference rules for analysis of causal relations between physiological events. The model is used to analyze physiological behavior, identify the effects of abnormalities, suggest appropriate therapies, and predict the results of therapy. This methodology integrates heuristic knowledge traditionally used in artificial intelligence programs with mathematical knowledge traditionally used in mathematical modeling programs. In recognition of its origins in artificial intelligence and mathematical modeling, the system is named AI/MM. This paper briefly introduces the knowledge representation and examples of the system analysis of behavior in the domain of renal physiology.

Journal ArticleDOI
TL;DR: The description and the formalisation of a model for reasoning by analogy based upon the similarity of proportions between specific elements of the object that is evaluated by a filtering operation established from a specific operation of matching.

Proceedings Article
08 Aug 1983
TL;DR: The knowledge representation used in UC provides a flexible framework suitable for a large variety of natural language processing tasks including parsing, inference, planning, goal analysis, and generation.
Abstract: The knowledge representation used in UC provides a flexible framework suitable for a large variety of natural language processing tasks including parsing, inference, planning, goal analysis, and generation. Although many of the knowledge structures are specific to the UNIX Consultant domain, a common design goal is the use of associative processing. By providing direct links between related knowledge structures, inference and other processing can be done very efficiently. Access to representations in UC is by hash indexing which simulates a real associative memory.

Journal ArticleDOI
TL;DR: An attempt is made to have a fresh look at the problem of knowledge representation as a whole and expresses a strong bias towards logic as a core formalism for representing and processing knowledge of any kind.

Journal ArticleDOI
TL;DR: An expert system to read speech sonagrams is built, thus modeling and putting to use the competence of expert phoneticians, and both the knowledge representation and the control strategy are presented.