Results for 'Knowledge-based system'

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  1. Knowledge Based System for Diagnosing Custard Apple Diseases and Treatment.Mustafa M. K. Al-Ghoul, Mohammed H. S. Abueleiwa, Fadi E. S. Harara, Samir Okasha & Samy S. Abu-Naser - 2022 - International Journal of Academic Engineering Research (IJAER) 6 (5):41-45.
    There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system correctly (...)
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  2. A Proposed Knowledge Based System for Desktop PC Troubleshooting.Ahmed Wahib Dahouk & Samy S. Abu-Naser - 2018 - International Journal of Academic Pedagogical Research (IJAPR) 2 (6):1-8.
    Abstract: Background: In spite of the fact that computers continue to improve in speed and functions operation, they remain complex to use. Problems frequently happen, and it is hard to resolve or find solutions for them. This paper outlines the significance and feasibility of building a desktop PC problems diagnosis system. The system gathers problem symptoms from users’ desktops, rather than the user describes his/her problems to primary search engines. It automatically searches global databases of problem symptoms and (...)
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  3. Knowledge Based System for Diabetes Diagnosis Using SL5 Object.Ibtesam M. Dheir, Alaa Soliman Abu Mettleq, Abeer A. Elsharif, Mohammed N. Abu Al-Qumboz & Samy S. Abu-Naser - 2019 - International Journal of Academic Pedagogical Research (IJAPR) 3 (4):1-10.
    Diabetes is a major public health issue that affects the nations of our time to a large extent and is described as a non-communicable epidemic. Diabetes mellitus is a common disease where there is too much sugar (glucose) floating around in your blood. This occurs because either the pancreas can’t produce enough insulin or the cells in body have become resistant to insulin. The concentration in this paper is on diagnosis diabetes by designing a proposed expert system. The main (...)
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  4. Knowledge Based System for the Diagnosis of Dengue Disease.Aysha I. Mansour & Samy S. Abu-Naser - 2019 - International Journal of Academic Health and Medical Research (IJAHMR) 3 (4):12-19.
    Background: Dengue Disease is a mosquito-borne tropical disease caused by the dengue virus, symptoms typically begin three to fourteen days after infection. This may include a high fever, headache, vomiting, muscle and joint pains, and a characteristic skin rash. Dengue serology is applied in different settings, such as for surveillance, in health care facilities in endemic areas and in travel clinics in non-endemic areas. The applicability and quality of serological tests in dengue endemic regions has to be judged against a (...)
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  5. Knowledge-based systems that determine the appropriate students major: In the faculty of engineering and information technology.Samy S. Abu Naser & Ihab S. Zaqout - 2016 - World Wide Journal of Multidisciplinary Research and Development 2 (10):26-34.
    In this paper a Knowledge-Based System (KBS) for determining the appropriate students major according to his/her preferences for sophomore student enrolled in the Faculty of Engineering and Information Technology in Al-Azhar University of Gaza was developed and tested. A set of predefined criterions that is taken into consideration before a sophomore student can select a major is outlined. Such criterion as high school score, score of subject such as Math I, Math II, Electrical Circuit I, and Electronics (...)
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  6. Isomorphism and legal knowledge based systems.T. J. M. Bench-Capon & F. P. Coenen - 1992 - Artificial Intelligence and Law 1 (1):65-86.
    This paper discusses some engineering considerations that should be taken into account when building a knowledge based system, and recommends isomorphism, the well defined correspondence of the knowledge base to the source texts, as a basic principle of system construction in the legal domain. Isomorphism, as it has been used in the field of legal knowledge based systems, is characterised and the benefits which stem from its use are described. Some objections to and (...)
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  7. Breast Cancer Knowledge Based System.Suheir H. Almurshidi & Samy S. Abu-Naser - 2018 - International Journal of Academic Health and Medical Research (IJAHMR) 2 (12):7-22.
    The Knowledge Based System for Diagnosing Breast Cancer is used to assist medical students to improve their education on diagnosis and counseling the process of analyzing the biopsy image of the microscope, determining the type of tumor and the treatment method for each case and identifying the disease related questions. According to the Ministry of Health in its annual report in Gaza, between 2009 and 2014 there are 7069 cases of breast cancer, and in 2014 there are (...)
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  8.  46
    Knowledge-based systems.Klaus Mainzer - 1990 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 21 (1):47-74.
    No kind of technology has had such a profound effect upon our lives and society as the new knowledge-based systems which start to overcome the traditional computer technology. Few areas of science raise such high expectations and meet with so much sceptical resistance as Artificial Intelligence (AI). So it is the task of philosophy of science and technology to analyze the factual methodological possibilities of AI-technology. After a historical sketch of AI-development (Chapter 2), the technological foundations of expert (...)
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  9.  36
    Knowledge-based systems in context: A methodological approach to the qualitative issues. [REVIEW]Galal H. Galal & Janet T. McDonnell - 1997 - AI and Society 11 (1-2):104-121.
    Knowledge-Based Systems (KBS) are developed to contain substantial elements of human knowledge and expertise in a well-defined domain, and use these to support user or expert tasks. Issues related to the social and organisational contexts of these systems are widely acknowledged to be particularly critical to their success. However, methodology proposals usually stop short of adequately handling soft and unstructured data that frame the contexts of use. The handling of qualitative data needs to be done in a (...)
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  10. Intelligent knowledge-based systems—AI in the UK In R. Kurzweil.B. W. Oakley - 1990 - In R. Kurzweil (ed.), The Age of Intelligent Machines. MIT Press. pp. 346--349.
  11.  25
    Knowledge-based systems and issues of integration: A commercial perspective. [REVIEW]Karl M. Wiig - 1988 - AI and Society 2 (3):209-233.
    Commercial applications of knowledge-based systems are changing from an embryonic to a growth business. Knowledge is classified by levels and types to differentiate various knowledge-based systems. Applications are categorized by size, generic types, and degree of intelligence to establish a framework for discussion of progress and implications. A few significant commercial applications are identified and perspectives and implications of these and other systems are discussed. Perspectives relate to development paths, delivery modes, types of integration, and (...)
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  12.  35
    Human-centred knowledge based systems design.Jon Young - 1989 - AI and Society 3 (2):80-87.
    It is held that the quality of the working environment afforded to an individual critically affects the health and well-being of that individual. This has consequences for both the quality of work which that individual can actually perform, and for the quality of the society in which that individual has a place. Conceptions of a fit working environment have led to the idea of a human-centred system, and this idea is applicable to the area of knowledge-based systems (...)
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  13.  13
    Acquiring Creative Knowledge for Knowledge-Based Systems.Z. Chen - 1996 - Journal of Intelligent Systems 6 (3-4):179-198.
  14.  1
    Building large knowledge-based systems: Representation and inference in the cyc project.John F. Sowa - 1993 - Artificial Intelligence 61 (1):95-104.
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  15.  2
    Building large knowledge-based systems: Representation and inference in the cyc project.Drew McDermott - 1993 - Artificial Intelligence 61 (1):53-63.
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  16.  14
    EDITORIAL: Recent Advances in Knowledge-Based Systems and their Applications.Bhanu Prasad - 2008 - Journal of Intelligent Systems 17 (1-3):1-4.
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  17.  7
    Building large knowledge-based systems: Representation and inference in the cyc project.Charles Elkan & Russell Greiner - 1993 - Artificial Intelligence 61 (1):41-52.
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  18.  11
    A framework for knowledge-based systems.Alexei Yu Muravitsky - 1996 - Journal of Applied Non-Classical Logics 6 (3):263-286.
  19.  10
    User Modelling in Knowledge-Based Systems.M. Felisa Verdejo - 1992 - In Jes Ezquerro (ed.), Cognition, Semantics and Philosophy. Kluwer Academic Publishers. pp. 23--46.
  20.  1
    Building large knowledge-based systems: Representation and inference in the cyc project.Robert Neches - 1993 - Artificial Intelligence 61 (1):65-79.
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    Building large knowledge-based systems: Representation and inference in the cyc project.Douglas Skuce - 1993 - Artificial Intelligence 61 (1):81-94.
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  22.  46
    Coupling hypertext and knowledge based systems: Two applications in the legal domain. [REVIEW]Paul Soper & Trevor Bench-Capon - 1993 - Artificial Intelligence and Law 2 (4):293-314.
    Hypertext and knowledge based systems can be viewed as complementary technologies, which if combined into a composite system may be able to yield a whole which is greater than the sum of the parts. To gain the maximum benefits, however, we need to think about how to harness this potential synergy. This will mean devising new styles of system, rather than merely seeking to enhance the old models.In this paper we describe our model for coupling hypertext (...)
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  23. Knowledge-based Intelligent Tutoring System for Teaching Mongo Database.Mohanad M. Hilles & Samy S. Abu Naser - 2017 - European Academic Research 4 (10).
    Recently, Intelligent Tutoring Systems (ITS) got much attention from researchers even though ITS educational technology began in the late 1960s and ITS is just embryonic from laboratories into the field. In this paper we outline an intelligent tutoring system for teaching basics of the databases system called (MDB). The MDB was built as education system by using the authoring tool (ITSB). MDB contains learning materials as a group of lessons for beginner level which include relational database (...) and lessons in the process to install and set up a database. MDB system has exams for each level of the Lessons. An evaluation was done to see the effectiveness the MDB among learners and instructors. The outcome of the evaluation was promising. (shrink)
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  24.  18
    Functional and Structural Integration without Competence Overstepping in Structured Semantic Knowledge Base System.Marek Krótkiewicz & Krystian Wojtkiewicz - 2014 - Journal of Logic, Language and Information 23 (3):331-345.
    Logic, language and information integration is one of areas broadly explored nowadays and at the same time promising. Authors use that approach in their 8 years long research into Structured Semantic Knowledge Base System. The aim of this paper is to present authors idea of system capable of generating synergy effect while storing various type of information. The key assumption, which has been adopted, is the thesis that the attempt to find universal way of the reality description (...)
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  25.  15
    Combining cases and rules to provide contextualised knowledge based systems.Debbie Richards - 2001 - In P. Bouquet V. Akman (ed.), Modeling and Using Context. Springer. pp. 465--469.
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  26. Advanced lexical ontologies and hybrid knowledge based systems: First steps to a dynamic legal electronic commentary. [REVIEW]Erich Schweighofer & Doris Liebwald - 2007 - Artificial Intelligence and Law 15 (2):103-115.
    Legal Information Retrieval (IR) research has stressed the fact that legal knowledge systems should be sufficiently capable to interpret and handle the semantics of a database. Modeling (expert-) knowledge by using ontologies enhances the ability to extract and exploit information from documents. This contribution presents theories, ideas and notions regarding the development of dynamic electronic commentaries based on a comprehensive legal ontology.
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  27.  13
    Automatic knowledge base refinement for classification systems.Allen Ginsberg, Sholom M. Weiss & Peter Politakis - 1988 - Artificial Intelligence 35 (2):197-226.
  28.  14
    The hybrid method of knowledge representation in a CAPP knowledge based system.Cezary Grabowik, Damian Krenczyk & Krzysztof Kalinowski - 2012 - In Emilio Corchado, Vaclav Snasel, Ajith Abraham, Michał Woźniak, Manuel Grana & Sung-Bae Cho (eds.), Hybrid Artificial Intelligent Systems. Springer. pp. 284--295.
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  29. Proceedings of the 11th IPMU Conference (Information Processing and Management of Uncertainty in Knowledge-Based Systems.Niki Pfeifer & G. D. Kleiter - 2006
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  30.  46
    An intellectual celebration: A review of the jurix legal knowledge based systems scholarship. [REVIEW]Abdul Paliwala - 2000 - Artificial Intelligence and Law 8 (4):317-335.
    The Foundation for Legal Knowledge Systems (JURIX) has, sinceits foundation in 1988, become an internationally renowned forumfor Law and Artificial Intelligence in theNetherlands. This paper is based onan intellectual review of the work of JURIX requested by theorganisation as part of its 10th anniversary in December 1997 andpresented as a keynote address at the 10th anniversary conference.It has been updated to include the following two conferences. Itapplauds the overall effort but also suggests some directions forfuture development and suggests (...)
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  31. A Three-Phase Approach to Building Knowledge-Based Systems.O. Laske - 1988 - Communication and Cognition: An Interdisciplinary Quarterly Journal 5:19-30.
     
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  32.  37
    LaBEO: A knowledge-based expert system for the “animation” of legal texts.Costantino Ciampi - 1987 - Theoria 3 (1):341-362.
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  33.  46
    The IKBALS project: Multi-modal reasoning in legal knowledge based systems. [REVIEW]John Zeleznikow, George Vossos & Daniel Hunter - 1993 - Artificial Intelligence and Law 2 (3):169-203.
    In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an (...)
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  34.  12
    Uncharted Aspects of Human Intelligence in Knowledge-Based “Intelligent” Systems.Ronaldo Vigo, Derek E. Zeigler & Jay Wimsatt - 2022 - Philosophies 7 (3):46.
    This paper briefly surveys several prominent modeling approaches to knowledge-based intelligent systems design and, especially, expert systems and the breakthroughs that have most broadened and improved their applications. We argue that the implementation of technology that aims to emulate rudimentary aspects of human intelligence has enhanced KBIS design, but that weaknesses remain that could be addressed with existing research in cognitive science. For example, we propose that systems based on representational plasticity, functional dynamism, domain specificity, creativity, and (...)
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  35.  37
    Roles for knowledge-based computer systems: Case studies in maternity care. [REVIEW]M. Harris, A. P. Jagodzinski & K. R. Greene - 2001 - AI and Society 15 (4):386-395.
    The design of medical knowledge-based computer systems requires effective interdisciplinary communication for the development of a community sharing common goals and a common language for design. Over the past 9 years the Perinatal Research Group, an interdisciplinary team of computer scientists, engineers and clinicians, have developed a prototype knowledge-based computer system to aid clinicians in the care of women in labour. The group were uncertain which approach to adopt to progress this system from a (...)
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  36. Knowledge Bases and Neural Network Synthesis.Todd R. Davies - 1991 - In Hozumi Tanaka (ed.), Artificial Intelligence in the Pacific Rim: Proceedings of the Pacific Rim International Conference on Artificial Intelligence. IOS Press. pp. 717-722.
    We describe and try to motivate our project to build systems using both a knowledge based and a neural network approach. These two approaches are used at different stages in the solution of a problem, instead of using knowledge bases exclusively on some problems, and neural nets exclusively on others. The knowledge base (KB) is defined first in a declarative, symbolic language that is easy to use. It is then compiled into an efficient neural network (NN) (...)
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  37.  9
    Knowledge caching for sensor-based systems.Yuval Roth & Ramesh Jain - 1994 - Artificial Intelligence 71 (2):257-280.
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  38.  14
    From expert systems to knowledge-based companies: How the AI industry negotiated a market for knowledge.Janet Vaux - 2001 - Social Epistemology 15 (3):231 – 245.
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  39. Knowledge, Discipline, System, Hope: The Fate of Metaphysics in the Doctrine of Method.Andrew Chignell - 2017 - In James O'Shea (ed.), Kant's Critique of Pure Reason: A Critical Guide. New York, USA: Cambridge University Press. pp. 259-279.
    In this chapter I highlight the apparent tensions between Kant’s very stringent critique of metaphysical speculation in the “Discipline of Pure Reason” chapter and his endorsement of Belief (Glaube) and hope (Hoffnung) regarding metaphysical theses in the subsequent “Canon of Pure Reason.” In the process I will examine his distinction between the theoretical and the practical bases for holding a “theoretical” conclusion (i.e. a conclusion about “what exists” rather than “what ought to be”) and argue that the position is subtle (...)
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  40.  11
    A Novel Method for Ranking Knowledge Organization Systems (KOSs) Based on Cognition States.Maziar Amirhosseini - 2023 - Knowledge Organization 49 (6):391-410.
    The purpose of this article is to delineate the process of evolution of know­ledge organization systems (KOSs) through identification of principles of unity such as internal and external unity in organizing the structure of KOSs to achieve content storage and retrieval purposes and to explain a novel method used in ranking of KOSs by proposing the principle of rank unity. Different types of KOSs which are addressed in this article include dictionaries, Roget’s thesaurus, thesauri, micro, macro, and meta-thesaurus, ontologies, and (...)
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  41.  13
    The Logic of Knowledge Bases.Hector J. Levesque & Gerhard Lakemeyer - 2001 - MIT Press.
    This book describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. The idea of knowledge bases lies at the heart of symbolic, or "traditional," artificial intelligence. A knowledge-based system decides how to act by running formal reasoning procedures over a body of explicitly represented knowledge—a (...) base. The system is not programmed for specific tasks; rather, it is told what it needs to know and expected to infer the rest. This book is about the logic of such knowledge bases. It describes in detail the relationship between symbolic representations of knowledge and abstract states of knowledge, exploring along the way the foundations of knowledge, knowledge bases, knowledge-based systems, and knowledge representation and reasoning. Assuming some familiarity with first-order predicate logic, the book offers a new mathematical model of knowledge that is general and expressive yet more workable in practice than previous models. The book presents a style of semantic argument and formal analysis that would be cumbersome or completely impractical with other approaches. It also shows how to treat a knowledge base as an abstract data type, completely specified in an abstract way by the knowledge-level operations defined over it. (shrink)
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  42.  21
    A Logic for Multiple-source Approximation Systems with Distributed Knowledge Base.Md Aquil Khan & Mohua Banerjee - 2011 - Journal of Philosophical Logic 40 (5):663-692.
    The theory of rough sets starts with the notion of an approximation space , which is a pair ( U , R ), U being the domain of discourse, and R an equivalence relation on U . R is taken to represent the knowledge base of an agent, and the induced partition reflects a granularity of U that is the result of a lack of complete information about the objects in U . The focus then is on approximations of (...)
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  43.  12
    Knowledge-Based Features for Place Classification of Unvoiced Stops.Preeti Rao & Veena Karjigi - 2013 - Journal of Intelligent Systems 22 (3):215-228.
    The classification of unvoiced stops in consonant–vowel syllables, segmented from continuous speech, is investigated by features related to speech production. As burst and vocalic transitions contribute to identification of stops in the CV context, features are computed from both regions. Although formants are the truly discriminating articulatory features, their estimation from the speech signal is a challenge especially in unvoiced regions like the release burst of stops. This may be compensated partially by sub-band energy-based features. In this work, formant (...)
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  44.  34
    Structural Inference from Conditional Knowledge Bases.Gabriele Kern-Isberner & Christian Eichhorn - 2014 - Studia Logica 102 (4):751-769.
    There are several approaches implementing reasoning based on conditional knowledge bases, one of the most popular being System Z (Pearl, Proceedings of the 3rd conference on theoretical aspects of reasoning about knowledge, TARK ’90, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, pp. 121–135, 1990). We look at ranking functions (Spohn, The Laws of Belief: Ranking Theory and Its Philosophical Applications, Oxford University Press, Oxford, 2012) in general, conditional structures and c-representations (Kern-Isberner, Conditionals in Nonmonotonic Reasoning (...)
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  45.  5
    Using empirical analysis to refine expert system knowledge bases.Peter Politakis & Sholom M. Weiss - 1984 - Artificial Intelligence 22 (1):23-48.
  46.  36
    Wissensbasierte Systeme — Utopie und RealitätKnowledge-based systems — Utopia and reality.Rolf Struve - 1992 - Journal for General Philosophy of Science / Zeitschrift für Allgemeine Wissenschaftstheorie 23 (2):315-322.
    The following article is a response to K. Mainzer's ‘Knowledge-Based Systems; Remarks on the Philosophy of Technology and Artificial Intelligence’. We show, that Mainzer does not reach any of his aimsto analyse the possibilities and limits of AI-technology.to reduce anxiousness and hostility against AI, which is motivated by phantastic speculations.to evaluate the factual impact of AI on our lives and on society.His article contributes on the contrary to phantastic speculations, which are not technologically justified in any way. There (...)
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  47.  11
    Enhancing the Efficiency of a Decision Support System through the Clustering of Complex Rule-Based Knowledge Bases and Modification of the Inference Algorithm.Agnieszka Nowak-Brzezińska - 2018 - Complexity 2018:1-14.
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  48. Shakespeare's Plays Weren't Written by Him, but by Someone Else of the Same Name an Essay on Intensionality and Frame-Based Knowledge Representation Systems.Douglas R. Hofstadter, Gray A. Clossman & Marsha J. Meredith - 1982 - Indiana University Linguistics Club.
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  49.  45
    Computer Reliability and Public Policy: Limits of Knowledge of Computer-Based Systems*: JAMES H. FETZER.James H. Fetzer - 1996 - Social Philosophy and Policy 13 (2):229-266.
    Perhaps no technological innovation has so dominated the second half of the twentieth century as has the introduction of the programmable computer. It is quite difficult if not impossible to imagine how contemporary affairs—in business and science, communications and transportation, governmental and military activities, for example—could be conducted without the use of computing machines, whose principal contribution has been to relieve us of the necessity for certain kinds of mental exertion. The computer revolution has reduced our mental labors by means (...)
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  50.  83
    Theory of Knowledge in System Dynamics Models.Mohammadreza Zolfagharian, Reza Akbari & Hamidreza Fartookzadeh - 2014 - Foundations of Science 19 (2):189-207.
    Having entered into the problem structuring methods, system dynamics (SD) is an approach, among systems’ methodologies, which claims to recognize the main structures of socio-economic behaviors. However, the concern for building or discovering strong philosophical underpinnings of SD, undoubtedly playing an important role in the modeling process, is a long-standing issue, in a way that there is a considerable debate about the assumptions or the philosophical foundations of it. In this paper, with a new perspective, we have explored theory (...)
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