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Relevant bibliographies by topics / Expert systems (Computer science) Case-based reasoning

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Academic literature on the topic 'Expert systems (Computer science) Case-based reasoning'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Expert systems (Computer science) Case-based reasoning"

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Zhou, Jingkai, Calvin G. Messersmith, and Janet D. Harrington. "HIDES: A Computer-Based Herbicide Injury Diagnostic Expert System." Weed Technology 19, no. 2 (June 2005): 486–91. http://dx.doi.org/10.1614/wt-04-185r1.

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Diagnosis of herbicide injury can be complex because of the large number and interaction of factors leading to herbicide injury. Computer-based expert systems have great potential to assist users, particularly nonexperts, in accurate diagnosis of herbicide injury. Rule-based and case-based reasoning are the most widely used forms of expert systems, and each system has strengths and limitations. Approaches that integrate rule-based and case-based reasoning may augment the positive aspects of the two reasoning methods and simultaneously minimize their negative aspects. The Herbicide Injury Diagnostic Expert System (HIDES) integrates rule-based and case-based reasoning and uses field-specific information, injury symptoms, herbicide use history, and herbicide information to diagnose crop injury from herbicides. The HIDES program uses a set of rules to identify suspect herbicide(s) that is the candidate for causing the observed injury and possible sources of the suspect herbicide(s). Case-based reasoning is used to propose a probable cause of injury by making an analogy to previously solved cases. A four-step procedure is followed when using HIDES: information collection, suspect herbicide identification, suspect herbicide source determination, injury reason suggestion, and knowledge accumulation.

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Vargas, Juan E., and Savita Raj. "Developing maintainable expert systems using case-based reasoning." Expert Systems 10, no. 4 (November 1993): 219–25. http://dx.doi.org/10.1111/j.1468-0394.1993.tb00101.x.

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Yang, Jie, Chenzhou Ye, and Xiaoli Zhang. "An Expert System Shell for Fault Diagnosis." Robotica 19, no. 6 (September 2001): 669–74. http://dx.doi.org/10.1017/s0263574701003460.

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Traditional expert systems for fault diagnosis have a bottleneck in knowledge acquisition, and have limitations in knowledge representation and reasoning. A new expert system shell for fault diagnosis is presented in this paper to develop multiple knowledge models (object model, rules, neural network, case-base and diagnose models) hierarchically based on multiple knowledge. The structure of the expert system shell and the knowledge representation of multiple models are described. Diagnostic algorithms are presented for automatic modeling and hierarchical reasoning. It will be shown that the expert system shell is very effective in building diagnostic expert systems.

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Rich, Steven H., and V. Venkatasubramanian. "Model-based reasoning in diagnostic expert systems for chemical process plants." Computers & Chemical Engineering 11, no. 2 (January 1987): 111–22. http://dx.doi.org/10.1016/0098-1354(87)80012-1.

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Yoshino, Hajime, and Katsumi Nitta. "Special Issue on AI and Law." Journal of Advanced Computational Intelligence and Intelligent Informatics 1, no. 2 (December 20, 1997): 81–82. http://dx.doi.org/10.20965/jaciii.1997.p0081.

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Lawyers use a reasoning process known as legal reasoning to solve legal problems. Legal expert systems could potentially help lawyers solve legal problems more quick and adequately, enable students to study law at school or at home more easily, and help legal scholars and professionals analyze the law and legal systems more clearly and precisely.In 1992, Hajime Yoshino of Meiji Gakuin University started a “Legal Expert Systems” project. This “Legal Expert” project is funded by the Japanese Ministry of Education, Science and Culture and is scheduled to run from May 1992 to March 1998. Yoshino organized over 30 lawyers and computer scientists to clarify legal knowledge and develop legal expert systems.This project covers a wide range of technologies such as the analysis of legal knowledge, the analysis of legal rules on international trade (United Nations Convention on Contracts for International Sale of Goods (CISG)), legal knowledge representation, legal inference models, utility programs to develop legal knowledge bases, and user interfaces. This project, which ends in March 1998, will focus on developing comprehensive legal expert systems as the final product. In this issue, we present 12 papers written by “Legal Expert” project members.In this number, Hajime Yoshino gives are overview of the legal expert systems project, explaining its aims, objectives, and organization. Six papers that follow his introduction include three on case-based reasoning. Legal rules are given by ambiguous predicates, making it difficult sometimes to determine whether conditions for rules are satisfied by the facts given of an event. In such cases, lawyers often refer to old cases and generate hypotheses through analogical reasoning.Kaoru Hirota, Hajime Yoshino and Ming Qiang Xu apply fuzzy theory to case-based reasoning. A number of related systems have been developed, but most focus on qualitative similarities between old cases and the current case, and cannot measure quantitative similarities. Hirota et al. treat quantitative similarity by applying fuzzy theory, explaining their method using CISG examples.Ken Satoh developed a way to compute an interpretation of undefined propositions in a legal rule using adversarial case-based reasoning. He translated old cases giving possible interpretations for a proposition into clauses in abductive logic programming and introduced abducibles to reason dynamically about important factors in an old case to the interpretation suiting the user’s purpose.Yoshiaki Okubo and Makoto Haraguchi formalized a way of attacking legal argument. Assume that an opponent has constructed a legal argument by applying a statute with an analogical interpretation. From the viewpoint of legal stability, the same statue for similar cases should be applied with the same interpretation. We thereby create a hypothetical case similar to the case in question and examine whether the statue can be interpreted analogically. Such a hypothetically similar case is created with the help of a goal-dependent abstraction framework. If a precedent in which a statue has been applied to a case with a different interpretation – particularly complete interpretation – can be found, the opponent’s argument is attacked by pointing out the incoherence of its interpretation of the statue.Takashi Kanai and Susumu Kunifuji proposed a legal reasoning system using abductive logic programming that deals with ambiguities in described facts and exceptions not described in articles. They examined the problems to be solved to develop legal knowledge bases through abductive logic programming, e.g., how to select ambiguities to be treated in abductive reasoning, how to describe time relationships, and how to describe an exception in terms of the application of abductive logic programming to legal reasoning.Toshiko Wakaki, Ken Satoh, and Katsumi Nitta presented an approach of reasoning about dynamic preferences in the framework of circumscription based on logic programming. To treat dynamic preferences correctly is required in legal reasoning to handle metarules such as lex posterior. This has become a hotly discussed topic in legal reasoning and more general nonmonotic reasoning. Comparisons of their method, Brewka’s approach, and Prakken and Sartor’s approach are discussed.Hiroyuki Matsumoto proposed a general legal reasoning model and a way of describing legal knowledge systematically. He applied his method to Japanese Maritime Traffic Law.Six more papers are to be presented in the next number

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Lee, Newton S. "A computational paradigm that integrates rule-based and model-based reasoning in expert systems." International Journal of Intelligent Systems 5, no. 2 (June 1990): 135–51. http://dx.doi.org/10.1002/int.4550050202.

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Moselhi, Osama, Tarek Hegazy, and Paul Fazio. "Potential applications of neural networks in construction." Canadian Journal of Civil Engineering 19, no. 3 (June 1, 1992): 521–29. http://dx.doi.org/10.1139/l92-061.

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During the past decade, several engineering disciplines, including construction, have embarked on developing “intelligent” decision support systems based on artificial intelligence (AI) techniques, including expert systems, symbolic knowledge representation, and logic programming. These systems attempt to capture the domain experts' intelligent behaviour and reasoning process utilized in decision-making, without regard to the underlying mechanisms producing that behaviour. This approach involves describing behaviours, usually with rules and symbols. In contrast, neural networks (NN), another AI-based technique that has been pursued on a large scale during the past few years, does not describe behaviours but rather imitate them. Neural networks are particularly superior to traditional expert systems in providing timely solutions based primarily on analogy with previous experience, rather than reasoning or computation. As such, neural networks have a great potential to work either as a supplement or as a complement to algorithmic and (or) other AI-based systems, providing more suitable tools for solving the industry ill-structured problems.This paper describes several characteristics of neural networks and outlines the advantages and limitations of commonly used NN paradigms. Potential applications of each paradigm in construction are identified. Two example applications are provided to demonstrate the problem-solving capabilities of neural networks: (i) estimation of hourly production rate of an excavation equipment; and (ii) estimation of productivity level for a construction trade. Future possibilities of integrating neural networks with other problem-solving techniques are described. Key words: construction, management techniques, neural networks, expert systems, pattern recognition, computer applications.

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Kwong, C. K., and G. F. Smith. "A computational system for process design of injection moulding: Combining blackboard-based expert system and case-based reasoning approach." International Journal of Advanced Manufacturing Technology 14, no. 4 (April 1998): 239–46. http://dx.doi.org/10.1007/bf01199878.

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Sokolov, I. A. "Theory and practice in artificial intelligence." Вестник Российской академии наук 89, no. 4 (April 24, 2019): 365–70. http://dx.doi.org/10.31857/s0869-5873894365-370.

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Artificial Intelligence is an interdisciplinary field, and formed about 60 years ago as an interaction between mathematical methods, computer science, psychology, and linguistics. Artificial Intelligence is an experimental science and today features a number of internally designed theoretical methods: knowledge representation, modeling of reasoning and behavior, textual analysis, and data mining. Within the framework of Artificial Intelligence, novel scientific domains have arisen: non-monotonic logic, description logic, heuristic programming, expert systems, and knowledge-based software engineering. Increasing interest in Artificial Intelligence in recent years is related to the development of promising new technologies based on specific methods like knowledge discovery (or machine learning), natural language processing, autonomous unmanned intelligent systems, and hybrid human-machine intelligence.

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Baaz, Matthias, and Gerald Quirchmayr. "Logic-based models of analogical reasoning: A fundamental basis for the design of legal expert systems." Expert Systems with Applications 4, no. 4 (January 1992): 369–78. http://dx.doi.org/10.1016/0957-4174(92)90130-k.

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Dissertations / Theses on the topic "Expert systems (Computer science) Case-based reasoning"

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Mick, Alan A. "Knowledge based text indexing and retrieval utilizing case based reasoning /." Online version of thesis, 1994. http://hdl.handle.net/1850/11715.

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Roth-Berghofer, Thomas R. "Knowledge maintenance of case-based reasoning systems : the SIAM methodology /." Berlin : Akad. Verl.-Ges. Aka, 2003. http://www.loc.gov/catdir/toc/fy0804/2008299389.html.

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Shi, Xun, and 施迅. "Integrating case-based reasoning and geographic information system forurban planning." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1997. http://hub.hku.hk/bib/B31221592.

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Shi, Xun. "Integrating case-based reasoning and geographic information system for urban planning /." Hong Kong : University of Hong Kong, 1997. http://sunzi.lib.hku.hk/hkuto/record.jsp?B21482421.

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Whitehead, John Douglass Hodjat. "Fault diagnosis based on causal reasoning." Thesis, Virginia Tech, 1987. http://hdl.handle.net/10919/40959.

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A "causal" expert system based on hypothetical reasoning and its application to a Mark 45 turret gun's lower hoist are described. HOIST is a system that performs fault diagnosis without the use of a domain expert or "shallow rules". Rather its "knowledge" is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the lesser acquainted diagnostician differs considerably from the normal rule-based expert system techniques: it reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, a functional model must reason about forces, fluid pressures and mechanical linkages, that is, qualitative physics. HOIST technology can be directly applied to any exactly specified device for modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in HOIST, has general utility in qualitative physics and reason maintenance.

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Wagner, Gerd. "Vivid logic : knowledge-based reasoning with two kinds of negation /." Berlin [u.a.] : Springer, 1994. http://www.loc.gov/catdir/enhancements/fy0815/93046747-d.html.

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Santosa, Purnomo Budi. "An academic advisory system : integrating an academic database management system, an expert system, and a case-based reasoning system /." [St. Lucia, Qld.], 2005. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe19071.pdf.

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Zhao, Wang. "Domain knowledge transformation (DKT) for conceptual design of mechanical systems /." free to MU campus, to others for purchase, 1997. http://wwwlib.umi.com/cr/mo/fullcit?p9841351.

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Steffens, Timo. "Enhancing similarity measures with imperfect rule-based background knowledge." Doctoral thesis, Berlin Aka, 2006. http://deposit.d-nb.de/cgi-bin/dokserv?id=2898562&prov=M&dok_var=1&dok_ext=htm.

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Suermondt, Henri Jacques. "Explanation in Bayesian belief networks." Full text available online (restricted access), 1992. http://images.lib.monash.edu.au/ts/theses/suermondt.pdf.

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Books on the topic "Expert systems (Computer science) Case-based reasoning"

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Case-based reasoning. San Mateo, CA: Morgan Kaufmann Publishers, 1993.

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D, Ashley K., and Bridge Derek G. 1963-, eds. Case-based reasoning research and development: 5th International Conference on Case-Based Reasoning, ICCBR 2003, Trondheim, Norway, June 23-26, 2003 : proceedings. Berlin: Springer, 2003.

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Goyal, Nita. A framework for reasoning precisely with vague concepts. Stanford, Calif: Dept. of Computer Science, Stanford University, 1994.

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Case-based maintenance of case-based reasoning systems in classification domains: Methods, implementation, and evaluation. Aachen: Shaker Verlag, 2005.

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Watson, Ian D. Applying case-based reasoning: Techniques for enterprise systems. San Francisco, Calif: Morgan Kaufmann, 1997.

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Steffens, Timo. Enhancing similarity measures with imperfect rule-based background knowledge. Berlin: Akademische Verlagsgesellschaft Aka, 2006.

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Roth-Berghofer, Thomas R. Knowledge maintenance of case-based reasoning systems: The SIAM methodology. Berlin: Aka - Akademische Verlagsgesellschaft, 2003.

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International Conference on Case-Based Reasoning (18th 2010 Alessandria, Italy). Case-based reasoning research and development: 18th International Conference on Case-Based Reasoning, ICCBR 2010, Alessandria, Italy, July 19-22, 2010 : proceedings. Berlin: Springer, 2010.

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International Conference on Case-Based Reasoning (2nd 1997 Providence, R.I.). Case-based reasoning: Research and development : Second International Conference on Case-Based Reasoning, ICCBR-97, Providence, RI, USA, July 25-27, 1997 : proceedings. Berlin: Springer, 1997.

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David, Hutchison. Case-Based Reasoning Research and Development: 8th International Conference on Case-Based Reasoning, ICCBR 2009 Seattle, WA, USA, July 20-23, 2009 Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009.

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Book chapters on the topic "Expert systems (Computer science) Case-based reasoning"

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Vilcahuamán, Raúl, Joaquim Meléndez, and Josep Lluis de la Rosa. "FUTURA: Hybrid System for Electric Load Forecasting by Using Case-Based Reasoning and Expert System." In Lecture Notes in Computer Science, 125–38. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36079-4_11.

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Dyne, M., C. Tsatsoulis, and J. Thorp. "Using inductive machine learning, expert systems and case based reasoning to predict preterm delivery in pregnant women." In Lecture Notes in Computer Science, 690–702. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58435-8_235.

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Pontet, T. "A constraint-based approach to uncertain and imprecise reasoning. Application to expert systems." In Lecture Notes in Computer Science, 277–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/3-540-54659-6_101.

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Abásolo, Chema, Enric Plaza, and Josep-Lluís Arcos. "Components for Case-Based Reasoning Systems." In Lecture Notes in Computer Science, 1–16. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-36079-4_1.

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Bonissone, Piero P., and Saad Ayub. "Similarity measures for case-based reasoning systems." In Lecture Notes in Computer Science, 159–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56735-6_53.

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Heister, Frank, and Wolfgang Wilke. "An architecture for maintaining case-based reasoning systems." In Lecture Notes in Computer Science, 221–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0056335.

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Schmidt, Rainer, Olga Vorobieva, and Lothar Gierl. "Adaptation Problems in Therapeutic Case-Based Reasoning Systems." In Lecture Notes in Computer Science, 992–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45224-9_132.

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Smiti, Abir, and Zied Elouedi. "Maintaining Case Based Reasoning Systems Based on Soft Competence Model." In Lecture Notes in Computer Science, 666–77. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07617-1_58.

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Legień, Grzegorz, Bartłomiej Śnieżyński, Dorota Wilk-Kołodziejczyk, Stanisława Kluska-Nawarecka, Edward Nawarecki, and Krzysztof Jaśkowiec. "Expert System with Web Interface Based on Logic of Plausible Reasoning." In Lecture Notes in Computer Science, 13–20. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22852-5_2.

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Plaza, Enric, and Josep-Lluís Arcos. "Towards a Software Architecture for Case-based Reasoning Systems." In Lecture Notes in Computer Science, 601–9. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-39963-1_63.

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Conference papers on the topic "Expert systems (Computer science) Case-based reasoning"

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Yu, Zhiqiang, Chun Hua Zhao, Dalin Zhu, and Mingsong Zhang. "Research on Case Representation in Printing Machine Fault Diagnosis Expert System Based on Case-Based Reasoning." In 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.736.

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Jiangping, Shi, Tong Weiguang, and Wang Daling. "Design of the Transformer Fault Diagnosis Expert System Based on Fuzzy Reasoning." In 2009 International Forum on Computer Science-Technology and Applications. IEEE, 2009. http://dx.doi.org/10.1109/ifcsta.2009.34.

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Gofuku, Akio, Atsushi Numoto, Hidekazu Yoshikawa, and Jiro Wakabayashi. "Estimation of Outline of Plant Behavior by Applying Case-Based Reasoning." In Expert Systems and Computer Simulation in Energy Engineering: Selected Papers from the Second International Forum. Connecticut: Begellhouse, 1992. http://dx.doi.org/10.1615/ichmt.1992.expsystcomputsimenergengin.450.

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Rahayu, Dyah Sulistyowati, and Heru Suhartanto. "Parallel Implementation in Case-based Reasoning Bankruptcy Prediction System." In 2020 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE, 2020. http://dx.doi.org/10.1109/icacsis51025.2020.9263170.

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Ferreira, Taís, and Márcia Aparecida Fernandes. "Students Group Formation Based on Case-Based Reasoning to Support Collaborative Learning." In 2020 Federated Conference on Computer Science and Information Systems. IEEE, 2020. http://dx.doi.org/10.15439/2020f166.

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Osuszek, Łukasz, and Stanisław Stanek. "Case Based Reasoning as an improvement of decision making and case processing in Adaptive Case Management systems." In 2015 Federated Conference on Computer Science and Information Systems. PTI, 2015. http://dx.doi.org/10.15439/2015f61.

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Pangjitt, Thitiphob, and Thanwadee Sunetnanta. "A model of ontology driven case-based reasoning for electronic issue management systems." In 2011 International Joint Conference on Computer Science and Software Engineering (JCSSE). IEEE, 2011. http://dx.doi.org/10.1109/jcsse.2011.5930099.

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Mendes, Joice Barbosa, and Alexandre Ramos. "A case-based reasoning framework for knowledge structuring: Structuring the domain module in intelligent tutoring systems." In 2012 IEEE International Conference on Computer Science and Automation Engineering (CSAE). IEEE, 2012. http://dx.doi.org/10.1109/csae.2012.6272986.

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