DOCSLIB.ORG

Chapter 6 - Expert Systems and Knowledge Acquisition

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chapter 6 - Expert Systems and Knowledge Acquisition Universiity of Pretoria etd – De Kock, E (2003) Chapter 6 - Expert Systems and knowledge acquisition An expert system’s major objective is to provide expert advice and knowledge in specialised situations (Turban 1995). ES is a sub-discipline of AI (Turban et al 2001). For an ES to reason, provide explanations and give advice, it needs to process and store knowledge. Knowledge in the form of facts and rules are kept in a knowledge base. 6.1 Expert System definitions ES definitions by Turban et al (2001) and Olson & Courtney (1992) were presented in Paragraph 2.3.1 (p19). Raggad & Gargano (1999) state: “ES emerged, as a branch of AI, from the effort of AI researchers to develop computer programs that could reason as humans”. Goodall (1985) distinguishes two approaches when defining an expert system: • The human/AI oriented approach, and • The technology oriented approach Goodall’s (1985) AI approach defines an expert system as a computer system that performs functions similar to those normally performed by a human expert. He extends this definition to include the nature of an expert system, in other words, he includes how ES behaves, in the technology approach: “An expert system is a computer system that uses a representation of human expertise in a specialist domain in order to perform functions similar to those normally performed by a human expert in that domain”. In formulating the above definition, Goodall (1985) focused on the implementation of two types of ES: • ES performing at a suitable ‘high’ expert level, and • ES that performs tasks which no human expert has, using perhaps a very complex set of rules to control a chemical process at a speed and in circumstances (e.g. temperature, time, location) which no human could possibly match. Such a system will most surely outperform the best human expert. The value of ES as a sub-discipline of AI (Turban et al 2001), can be appreciated more when comparing AI to natural intelligence and conventional programs. Kaplan (1984) as referred to by Turban et al (2001), states several commercial advantages of AI over natural intelligence: • AI is more permanent. Employees take knowledge with them when they leave their place of employment, or worse: an employee may even forget his knowledge. As long as the computer systems and programs remain unchanged, the knowledge remains the same. 107 Universiity of Pretoria etd – De Kock, E (2003) • AI offers ease of duplication and dissemination. Transferring knowledge between persons may be a lengthy process. Some expertise is never transferred. Knowledge embodied in a computer system can be easily moved to another computer. • AI can be less expensive than natural intelligence. Buying computer services can cost less than having a human performing the same task. • AI is a computer technology and thus consistent and thorough. Natural intelligence is erratic because people are erratic and perform inconsistently. , and • AI can be documented. Decisions made by a computer can easily be documented by tracing the activities of the system. Natural intelligence is difficult to document. A conclusion reached may be difficult to be recreated or recalled later. Some of the assumptions or even the reasoning process may be forgotten. Natural intelligence has several advantages over AI: • Natural intelligence is creative: In an AI system, knowledge has to be carefully constructed, while knowledge is inherent to all human beings • Natural intelligence uses sensory experienced directly: Users using AI, use sensory experiences indirectly • Natural intelligence enables people to recognise relationships between artefacts, sense qualities and spot patterns that explain how various items interrelate, and • A wide context of experiences is used when natural intelligence solves an individual problem, while AI typically gains its power by having a very narrow focus ES differs from conventional programs too. Raggad & Gargano (1999) and Turban et al (2001) summarise the characteristics of conventional programs as follows: • Conventional Computer Based Information Systems (CBIS) processes data and information as a direct conceptual resource • Conventional programs are based on algorithms or mathematical formulas and sequential procedures that lead to a solution using data to solve problems • The objective of CBIS is to create and efficient solution by designing an efficient algorithm • CBIS are characterised by repetitive processing • The processing system or control mechanism of the system and the knowledge of the subject are intertwined as part of the sequence of instructions • Knowledge of the subject is built into the processing mechanism or control of the system. The subject knowledge or business rules form part of the sequence of instructions. All business rules are part of the program code. The knowledge is stored as control embedded in the program instructions. The control is the sequence in which the transactions are performed. The programmer fixes these steps in advance. It is called the algorithm of the program. • To change the business rules or domain knowledge a programmer needs to edit the program and insert or change the statements that represent the information in the correct place 108 Universiity of Pretoria etd – De Kock, E (2003) • The program or system used does not explain which rules were used during processing of data to produce the output • The program is specialised to deal with specific information. A complete system of programs and input are necessary to achieve something useful. • One missing essential step causes the program(s) to malfunction. The data that are processed should be complete and certain to achieve an appropriate result or valid output. • Error detection of the domain knowledge is performed when executing the program and noticing faulty output • CBIS frequently uses algorithms in its search approach ,and • CBIS has no reasoning capability except for a fixed process in the algorithm of the program, and • Updating and maintenance of data and information are usually difficult When with conventional programs, the characteristics of ES are: • The main conceptual source of ES is knowledge. ES can expand to include a knowledge acquisition component that processes data and information into rules. Data and information are indirect conceptual resources. • ES does not represent its knowledge in algorithmic-like procedures. AI is based on symbolic processing of knowledge. Knowledge is represented by symbols such as letters, words or numbers that represent objects, processes and their relationships. The knowledge base contains facts and concepts and relationships among them. Processes are used to manipulate the symbols in the knowledge base to generate advice or recommendations to solve problems. • The objective of ES is to create an efficient knowledge representation to support the inference mechanism • ES keeps the control separate from the rules: the inference engine is responsible for most of the control. The knowledge base contains the rules or knowledge. • Knowledge is expressed as data and not encoded in the control of the ES. This has the advantage that knowledge can be returned to the user as explanations of the ES conclusions. • The order of the rules in the knowledge base does not matter • ES provides an explanation facility • The rules that comprise the knowledge may be built bit-by-bit, a rule at a time. ES can tolerate imperfect, incomplete or uncertain business rules or domain knowledge. • The ES will produce results even if the knowledge base is incomplete and uncertain. This is because of the separation of the knowledge and control components. • ES frequently uses heuristics in its search approach • ES has a reasoning capability, and • Maintenance and updating of data are relatively easy. Changes can be made in self-contained modules. 109 Universiity of Pretoria etd – De Kock, E (2003) 6.1.1 When to use an Expert System ES is appropriate for a limited number of problem domains. The task should be well bounded and should not include a large number of event combinations. A general rule of thumb is that if the average employee can solve the problem in a few minutes it is not worth solving it by an expert system. ES is appropriate (Raggad & Gargano 1999; Mallach 1994): • If great costs are involved when making the decision • When applied to repetitive problem domains, the task must be performed often • When a big difference exists between the best solution and the worst solution • When test data is easy available to test and validate the ES • When there is a general agreement on the system’s conclusions. Errors in the input should be tolerated in the system output. Experts must agree on the solutions. • When recognised expertise is available and a necessity during the development of the ES • If the problem is clearly specifiable in the area of expertise i.e. what the system is supposed to do before it is done, the desired system can be developed • If the problem is identifiable with a human expert, that is based on human expertise. Experts must perform the task substantially better than non-experts. The human expertise must be scarce. • If the problem domain is well bounded: for example defining criteria to determine the subject matter within the system from the matter outside of the system: The task is reasonably stable and within an acceptable narrow domain. • If solving the problem is based on knowledge rather than common sense reasoning: ES is based on knowledge and naïve systems on reasoning. • The solution has to be possible, justifiable and concisely generated (using only a few hundred rules), and • The knowledge has to be of a cognitive style and independent of common sense. Physical activities are ruled out. Goodall (1985) provides a comprehensive list of suitable types of tasks appropriate using ES in solving problems that includes: • Interpretation • Prediction • Diagnosis • Debugging • Design • Planning • Monitoring • Instruction, and • Control 110 Universiity of Pretoria etd – De Kock, E (2003) Turban et al (2001) calls the above-mentioned types the generic categories of Expert Systems.
Load more

Recommended publications
  • Theory-Based Knowledge Acquisition for Ontology Development
    UNIVERSITÄT HOHENHEIM Theory-based Knowledge Acquisition for Ontology Development A dissertation submitted to the Faculty of Business, Economics and Social Sciences of the University of Hohenheim in partial fulfillment of the requirements for the degree of Doctor of Economics (Dr. oec.) by Andreas Scheuermann July 2016 Principal advisor: Prof. Dr. Stefan Kirn Co-advisor: Prof. Dr. Ralph Bergmann Chair of the examination committee: Prof. Dr. Christian Ernst Dean: Prof. Dr. Dirk Hachmeister Date of oral examination: 7. July 2016 Abstract iii Abstract This thesis concerns the problem of knowledge acquisition in ontology development. Knowledge acquisition is essential for developing useful ontologies but it is a complex and error-prone task. When capturing specific knowledge about a particular domain of interest, the problem of knowledge acquisition occurs due to linguistic, cognitive, modelling, and methodical difficulties. For overcoming these four difficulties, this research proposes a theory-based knowledge acquisition method. By studying the knowledge base, basic terms and concepts in the areas of ontology, ontology development, and knowledge acquisition are defined. A theoretical analysis of knowledge acquisition identifies linguistic, cognitive, modelling, and methodical difficulties, for which a survey of 15 domain ontologies provides further empirical evidence. A review of existing knowledge acquisition approaches shows their insufficiencies for reducing the problem of knowledge acquisition. As the underpinning example, a description of the domain of transport chains is provided. Correspondingly, a theory in business economics, i.e. the Contingency Approach, is selected. This theory provides the key constructs, relationships, and dependencies that can guide knowledge acquisition in the business domain and, thus, theoretically substantiate knowledge acquisition.
    [Show full text]
  • Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing
    Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) Thesis for the Degree of Doctor of Philosophy 2017 Propositional Knowledge: Acquisition and Application to Syntactic and Semantic Parsing Bernardo Cabaleiro Barciela University Master's Degree in Languages and Computer Systems (National Distance Education University) Doctoral Programme in Intelligent Systems Academic advisor: Anselmo Peñas Padilla Associate Professor in the Languages and Computer Systems Department (National Distance Education University) A mi familia Acknowledgements Esta tesis pone punto final a mis estudios de doctorado, y, a la vez, supone el fin de una bonita etapa de mi vida. A lo largo de este tiempo me han acompañado muchas personas que han hecho que esta experiencia sea inolvidable. Estas líneas son para intentar agradecerles todo lo que han hecho por mí. En primer lugar, me gustaría mostrar mi agradecimiento a Anselmo Peñas por su dirección tanto del trabajo de fin de máster como de esta tesis doctoral. Durante todo este tiempo Anselmo ha puesto mucho empeño para formarme, no sólo en el campo del PLN sino también como investigador. Quiero agradecerle la libertad que me ha dado para trabajar en lo que me interesaba, y el especial cuidado que ha puesto en corregir y mejorar esos trabajos. Me gustaría dar las gracias también a mis compañeros en el departamento de lenguajes y sistemas, tanto a estuvieron como a los que están.
    [Show full text]
  • A Knowledge Acquisition Assistant for the Expert System Shell Nexpert-Object
    Rochester Institute of Technology RIT Scholar Works Theses 1988 A knowledge acquisition assistant for the expert system shell Nexpert-Object Florence Perot Follow this and additional works at: https://scholarworks.rit.edu/theses Recommended Citation Perot, Florence, "A knowledge acquisition assistant for the expert system shell Nexpert-Object" (1988). Thesis. Rochester Institute of Technology. Accessed from This Thesis is brought to you for free and open access by RIT Scholar Works. It has been accepted for inclusion in Theses by an authorized administrator of RIT Scholar Works. For more information, please contact [email protected]. Rochester Institute of Technology School of Computer Science and Technology A KNOWLEDGE ACQUISITION ASSISTANT FOR THE EXPERT SYSTEM SHELL NEXPERT-OBJECTTM By FLORENCE C. PEROT A thesis, submitted to The Faculty of the School ofCornputer Science and Technology In partial fulfillment of the requ.irements for the degree of Master of Science i.n COF~?uter Science Approved by: 'lIfer! r~ Pro John A. Biles (ICSG Department, R.I.TJ Date Dr. Alain T. Rappaport (President, Neuron Data) Date Dr. Peter G. Anderson (ICSG Chairman, R.I.T.) ;7 1J'{A{e? 'I.-g ROCHESTER, NEW-YORK August, 1988 ABSTRACT This study addresses the problems of knowledge acquisition in expert system development examines programs whose goal is to solve part of these problems. Among them are knowledge acquisition tools, which provide the knowledge engineer with a set of Artificial Intelligence primitives, knowledge acquisition aids, which offer to the knowledge engineer a guidance in knowledge elicitation, and finally, automated systems, which try to replace the human interviewer with a machine interface.
    [Show full text]
  • Knowledge Acquisition in a System
    Wright State University CORE Scholar Browse all Theses and Dissertations Theses and Dissertations 2012 Knowledge Acquisition in a System Christopher J. Thomas Wright State University Follow this and additional works at: https://corescholar.libraries.wright.edu/etd_all Part of the Computer Engineering Commons, and the Computer Sciences Commons Repository Citation Thomas, Christopher J., "Knowledge Acquisition in a System" (2012). Browse all Theses and Dissertations. 651. https://corescholar.libraries.wright.edu/etd_all/651 This Dissertation is brought to you for free and open access by the Theses and Dissertations at CORE Scholar. It has been accepted for inclusion in Browse all Theses and Dissertations by an authorized administrator of CORE Scholar. For more information, please contact [email protected]. Knowledge Acquisition in a System A thesis submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy by Christopher J. Thomas B.S., Universitat¨ Koblenz 2012 Department of Computer Science and Engineering Wright State University Wright State University GRADUATE SCHOOL January 9, 2013 I HEREBY RECOMMEND THAT THE THESIS PREPARED UNDER MY SUPER- VISION BY Christopher J. Thomas ENTITLED Knowledge Acquisition in a System BE ACCEPTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DE- GREE OF Doctor of Philosophy in Computer Science. Amit P. Sheth, Ph.D. Thesis Director Mateen Rizki, Ph.D. Chair, Department of Computer Science and Engineering Committee on Final Examination Andrew Hsu, Ph.D. Dean, Graduate School Amit P. Sheth, Ph.D. Pankaj Mehra, Ph.D. Shaojun Wang, Ph.D. Pascal Hitzler, Ph.D. Gerhard Weikum, Ph.D. ABSTRACT Thomas, Christopher. PhD, Department of Computer Science and Engineering, Wright State Uni- versity, 2012.
    [Show full text]
  • Medical Knowledge Acquisition Using Biomedical
    MEDICAL KNOWLEDGE ACQUISITION USING BIOMEDICAL KNOWLEDGE RESOURCES FOR DISEASE-SPECIFIC ONTOLOGIES by Liqin Wang A dissertation submitted to the faculty of The University of Utah in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Biomedical Informatics The University of Utah May 2017 Copyright © Liqin Wang 2017 All Rights Reserved The University of Utah Graduate School STATEMENT OF DISSERTATION APPROVAL The dissertation of Liqin Wang has been approved by the following supervisory committee members: Peter J. Haug , Chair 03/14/2017 Date Approved Bruce E. Bray , Member 03/14/2017 Date Approved Guilherme Del Fiol , Member 03/13/2017 Date Approved Olivier Bodenreider , Member 03/07/2017 Date Approved Wendy W. Chapman , Member 03/13/2017 Date Approved and by Wendy W. Chapman , Chair/Dean of the Department/College/School of Biomedical Informatics and by David B. Kieda, Dean of The Graduate School. ABSTRACT Disease-specific ontologies, designed to structure and represent the medical knowledge about disease etiology, diagnosis, treatment, and prognosis, are essential for many advanced applications, such as predictive modeling, cohort identification, and clinical decision support. However, manually building disease-specific ontologies is very labor-intensive, especially in the process of knowledge acquisition. On the other hand, medical knowledge has been documented in a variety of biomedical knowledge resources, such as textbook, clinical guidelines, research articles, and clinical data repositories, which offers a great opportunity for an automated knowledge acquisition. In this dissertation, we aim to facilitate the large-scale development of disease-specific ontologies through automated extraction of disease-specific vocabularies from existing biomedical knowledge resources.
    [Show full text]
  • Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources Demeke Asres Ayele
    Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources Demeke Asres Ayele To cite this version: Demeke Asres Ayele. Knowledge Acquisition Framework from Unstructured Biomedical Knowledge Sources. Information Retrieval [cs.IR]. Université d’Addis Abeba, 2016. English. tel-02087577 HAL Id: tel-02087577 https://tel.archives-ouvertes.fr/tel-02087577 Submitted on 18 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ADDIS ABABA UNIVERSITY SCHOOL OF GRADUATE STUDIES KNOWLEDGE ACQUISITION FRAMEWORK FROM UNSTRUCTURED BIOMEDICAL KNOWLEDGE SOURCES DEMEKE ASRES AYELE A THESIS SUBMITTED TO IT DOCTORAL PROGRAM ADDIS ABABA UNIVERSITY PRESENTED IN FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY IN INFORMATION TECHNOLOGY (LANGUAGE TECHNOLOGY) Jury: Prof. Luciano Serafìni External Examiner Dr. Solonron Tefera Internal Examiner Dr. Jean-Pierre Chevallet Supervisor Dr. Getnet Mitikie Co-Supervisor Dr. Milion Mcshesha Co-Supervisor Dr. Dida Midekso Chairman ADDIS ABABA, ETHIOPIA 9 August 2016 Knowledge Acquisition
    [Show full text]
  • Knowledge Acquisition, Representation, and Reasoning
    TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-174 CHAPTER Knowledge 18 Acquisition, Representation, and Reasoning Learning Objectives ◆ Understand the nature of knowledge ◆ Understand the knowledge-engineering process ◆ Learn different approaches to knowledge acquisition ◆ Explain the pros and cons of different knowledge acquisition approaches ◆ Illustrate methods for knowledge verification and validation ◆ Understand inference strategies in rule-based intelligent systems ◆ Explain uncertainties and uncertainty processing in expert systems (ES) n this book, we have described the concepts and structure of knowledge-based ES. IFor such systems to be useful, it is critical to have powerful knowledge in the knowl- edge base and a good inference engine that can derive valid conclusions from the knowledge base. In this chapter, we describe the process for acquiring knowledge from human experts, validating and representing the knowledge, creating and operating inference mechanisms, and dealing with uncertainty.The chapter is divided into the fol- lowing sections: W18.1 Opening Vignette: Development of a Real-Time Knowledge-Based System at Eli Lilly W18.2 Concepts of Knowledge Engineering W18.3 The Scope and Types of Knowledge W18.4 Methods of Acquiring Knowledge from Experts W18.5 Acquiring Knowledge from Multiple Experts W18.6 Automated Knowledge Acquisition from Data and Documents W18.7 Knowledge Verification and Validation W18.8 Representation of Knowledge W18.9 Reasoning in Intelligent Systems W-174 TURBMW18_0131986600.QXD 11/9/06 8:58 PM Page W-175 Opening Vignette W-175 ◆ W18.10 Explanation and Metaknowledge W18.11 Inferencing with Uncertainty W18.12 Expert Systems Development W18.13 Knowledge Acquisition and the Internet W18.1 OPENING VIGNETTE: DEVELOPMENT OF A REAL-TIME KNOWLEDGE-BASED SYSTEM AT ELI LILLY PROBLEM Eli Lilly (lilly.com) is a large U.S.-based, global pharmaceutical manufacturing company that has 42,600 employees worldwide and markets it products in about 160 countries.
    [Show full text]
  • A Brief French History of Knowledge Acquisition Nathalie Aussenac-Gilles, Fabien Gandon
    From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition Nathalie Aussenac-Gilles, Fabien Gandon To cite this version: Nathalie Aussenac-Gilles, Fabien Gandon. From the knowledge acquisition bottleneck to the knowl- edge acquisition overflow: A brief French history of knowledge acquisition. International Journal of Human-Computer Studies, Elsevier, 2013, vol. 71 (n° 2), pp. 157-165. 10.1016/j.ijhcs.2012.10.009. hal-01124416 HAL Id: hal-01124416 https://hal.archives-ouvertes.fr/hal-01124416 Submitted on 6 Mar 2015 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Open Archive TOULOUSE Archive Ouverte (OATAO) OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 12333 To link to this article : DOI :10.1016/j.ijhcs.2012.10.009 URL : http://dx.doi.org/10.1016/j.ijhcs.2012.10.009 To cite this version : Aussenac-Gilles, Nathalie and Gandon, Fabien From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition.
    [Show full text]
  • Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-Of-The-Art
    Machine Learning, 4, 37%394 (1989) © 1989 Kluwer Academic Publishers, Boston. Manufactured in The Netherlands. Knowledge Acquisition for Knowledge-Based Systems: Notes on the State-of-the-Art JOHN H. BOOSE Advanced Technology Center, Boeing Computer Services, 7L-64, PO Box 24346, Seattle, Washington, 98124, USA BRIAN R. GAINES Department of Computer Science, University of Calgary, 2500 University Dr. NW, Calgary, Alberta, Canada T2N 1N4 Abstract. Notes from the organizers of a series of knowledge acquisition workshops are presented here. The state-of-the-art in knowledgeacquisition research is briefly described. Then the technologyof interactive knowledge acquisition is discussed, including a descriptive framework, dimensions of use, and research patterns. Finally, dissemination of information from knowledge acquisition workshops is detailed. Key Words: knowledgeacquisition, knowledgeacquisition tools, knowledgeacquisition methods, knowledge-based systems, expertise transfer, knowledge elicitation. 1. Knowledge Acquisition--State-of-the-Art At the time of the first AAAI Sponsored Workshop on Knowledge Acquisition for Knowledge- Based Systems in Banff, November 1986, the literature on knowledge acquisition was sparse, scattered in conference publications, and difficult to access. The 1986 Workshop and the ensuing journal publication of some forty papers representing the state-of-the-art world-wide changed that situation. The inclusion of sessions on knowledge acquisition in many confer- ences, the European Workshops on Knowledge Acquisition for Knowledge-Based Systems in London and Reading, September 1987, Bonn in June 1988 and Paris in July 1989, the Banff Workshops in October 1987, November 1988 and October 1989, and the establishment of a new knowledge acquisition journal (see below) have changed the situation even further.
    [Show full text]
  • A Survey on Domain Knowledge Representation with Frames
    A Survey on Domain Knowledge Representation with Frames Vladislavs Nazaruks and Jānis Osis Department of Applied Computer Science, Riga Technical University, Sētas iela 1, LV-1048, Riga, Latvia Keywords: System Modelling, Frames, Knowledge Base. Abstract: Domain knowledge acquisition, presentation and maintenance play an important role in software development. Frame-based knowledge bases are used to support the decision-making process. We believe that a use of a knowledge base that supports model transformations is not less important. To clarify the current state of a use of frame systems we have investigated recent research in the field to find out about techniques used for knowledge acquisition, frame elements, implementation technologies, existing limitations in implementation and integration with other knowledge representation formats. The overview showed that knowledge acquisition often is manual, procedural knowledge in frames can be separated, web-enabled knowledge bases are the trend, and the frame systems can be used in hybrid knowledge bases. However, some limitations in performance and integration with other knowledge representation systems exist due to support of different world paradigms. The obtained results show that despite existing limitations, frame-based knowledge systems still are in use and researchers found ways how to adapt them to the modern requirements. 1 INTRODUCTION interoperability that uses models to express and direct the course of understanding, requirements elicitation, One of unsolved issues in software development is design, construction, deployment, operation, inconsistency between the problem and its solution. maintenance, and modification” (OMG 2010). The Some of the causes are insufficient understanding of key principle of the MDA is a separation of concerns characteristics of the problem domain, their in specifications.
    [Show full text]
  • Information Integration for Concurrent Engineering (Iice) Idef3 Process Description Capture Method Report
    AL-TR-1995-XXXX INFORMATION INTEGRATION FOR CONCURRENT ENGINEERING (IICE) IDEF3 PROCESS DESCRIPTION CAPTURE METHOD REPORT Richard J. Mayer, Ph.D. Christopher P. Menzel, Ph.D. Michael K. Painter Paula S. deWitte, Ph.D. Thomas Blinn Benjamin Perakath, Ph.D. KNOWLEDGE BASED SYSTEMS, INCORPORATED ONE KBSI PLACE 1500 UNIVERSITY DRIVE EAST COLLEGE STATION, TEXAS 77840-2335 HUMAN RESOURCES DIRECTORATE LOGISTICS RESEARCH DIVISION SEPTEMBER 1995 INTERIM TECHNICAL REPORT FOR PERIOD APRIL 1992 - SEPTEMBER 1995 Approved for public release; distribution is unlimited. September 1995 Interim - February 1991 to September 1995 IDEF3 Process Description Capture Method Report Richard J. Mayer, Ph.D. Michael K. Painter Christopher P. Menzel, Ph.D. Benjamin Perakath, Ph.D Paula S. deWitte, Ph.D. Thomas Blinn Knowledge Based Systems, Inc. One KBSI Place, 1500 University Drive East College Station, TX 77845 KBSI-IICE-90-STR-01-0592-02 Armstrong Laboratory Human Resources Directorate Logistics Research Division Wright-Patterson AFB, OH 45433 AL-TR-1995-XXXX Approved for Public Release; distribution is unlimited A This document provides a method overview, practice and use description, and language reference for the Integration Definition (IDEF) method for Process Description Capture (IDEF3). IDEF3 is designed to help document and analyze the processes of an existing or proposed system. Proven guidelines and a language for information capture help users capture and organize process information for multiple downstream uses. IDEF3 supports both process-centered and object-centered knowledge acquisition strategies enabling users to capture assertions about real-world processes and events in ways paralleling common forms of human expression. IDEF3 includes the ability to capture and structure descriptions of how a system works from multiple viewpoints.
    [Show full text]
  • A Multi-Disciplinary Review of Knowledge Acquisition Methods: from Human to Autonomous Eliciting Agents
    A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents George Leu1, Hussein Abbass School of Engineering and IT, UNSW Canberra, Australia Abstract This paper offers a multi-disciplinary review of knowledge acquisition methods in human activity systems. The review captures the degree of involvement of various types of agencies in the knowledge acquisition process, and proposes a classification with three categories of methods: the human agent, the human-inspired agent, and the autonomous machine agent methods. In the first two categories, the acquisition of knowledge is seen as a cognitive task analysis exercise, while in the third category knowledge acquisition is treated as an autonomous knowledge-discovery endeavour. The motivation for this classification stems from the continuous change over time of the structure, meaning and purpose of human activity systems, which are seen as the factor that fuelled researchers’ and practitioners’ efforts in knowledge acquisition for more than a century. We show through this review that the KA field is increasingly active due to the higher and higher pace of change in human activity, and conclude by discussing the emergence of a fourth category of knowledge acquisition methods, which are based on red-teaming and co-evolution. Keywords: knowledge acquisition, human agency, machine agency, cognitive task analysis, autonomous knowledge-discovery List of abbreviations The entities the KA can be performed on fall into three major categories - natural, man-made and humans - further referred to KA Knowledge Acquisition throughout the paper as natural systems, technical systems, and HAS Human Activity System human activity systems. An example of KA applied to natu- CTA Cognitive Task Analysis ral systems can be the weather cycle in a certain region of the SME Subject Matter Expert Earth, which one needs to understand in order to ensure safe air- KD Knowledge Discovery craft operation over that region in different periods of the year.
    [Show full text]