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Application of ALSV(FD) logic and XTT knowledge representation in the range of ADI properties

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The objective of studies presented in this publication was structuring of research knowledge about the ADI functional properties and changes in these properties due to material treatment. The results obtained were an outcome of research on the selection of a format of knowledge representation that would be useful in further work aiming at the design, application and implementation of an effective system supporting the decisions of a technologist concerning the choice of a suitable material (ADI in this case) and appropriate treatment process (if necessary). ALSV(FD) logic allows easy modelling of knowledge, which should let addressees of the target system carry out knowledge modelling by themselves. The expressiveness of ALSV (FD) logic allows recording the values of attributes from the scope of the modelled domain regarding ADI, which is undoubtedly an advantage in the context of further use of the logic. Yet, although the logic by itself does not allow creating the rules of knowledge, it may form a basis for the XTT format that is rule-based notation. The difficulty in the use of XTT format for knowledge modelling is acceptable, but formalism is not suitable for the discovery of rules, and therefore the knowledge of technologist is required to determine the impact of process parameters on values that are functional properties of ADI. The characteristics of ALSV(FD) logic and XTT formalism, described in this article, cover the most important aspects of a broadly discussed, full evaluation of the applicability of these solutions in the construction of a system supporting the decisions of a technologist.
Rocznik
Strony
75--78
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
autor
  • AGH, University of Science and Technology, Cracow, Poland;
  • Foundry Research Institute, Cracow, Poland;
  • AGH University of Science and Technology, Cracow, Poland;
autor
  • AGH University of Science and Technology, Cracow, Poland;
  • Foundry Research Institute, Cracow, Poland;
autor
  • AGH University of Science and Technology, Cracow, Poland;
Bibliografia
  • [1] Kowalski, A., Kluska-Nawarecka S. & Regulski, K. (2013). ADI after austenitising from intercritical temperature. Archives of Foundry Engineering. 13(1), 81–88.
  • [2] Myszka, D. & Bombiński, S. (2014). Preliminary evaluation of the applicability of F, V and AEsignals in diagnosis of ADI machining process. Archives of Foundry Engineering. 14(1), 91–96.
  • [3] Krzyńska, A. (2013). Searching for Better Properties of ADI. Archives of Foundry Engineering. 13(1), 91–96.
  • [4] Mrzygłód, B. & Regulski, K. (2011). Model of knowledge representation about materials in the form of a relational database for CAPCAST system. Archives of Foundry Engineering. 11(3), 81–8.
  • [5] Kluska-Nawarecka S., Mrzygłód B., Durak J. & Regulski K. (2010). Analysis of the applicability of domain ontology in copper alloys processing. Archives of Foundry Engineering. 10(2), 69-74.
  • [6] Ligęza, A. (2006). Logical Foundations for Rule-Based Systems. (2nd ed.). Springer-Verlag.
  • [7] Nalepa, G., Ligęza, A. (2008). XTT+ Rule Design Using the ALSV(FD). In the 2nd East European Workshop on Rule-Based Applications (RuleApps 2008) at the 18th European Conference on Artificial Intelligence, 23 July 2008 (pp. 11-15). Patras, Greece.
  • [8] Nalepa, G., Ligęza, A. (2009). On ALSV Rules Formulation and Inference. In the Twenty-Second International Florida Artificial Intelligence Research Society Conference, 19-21 May 2009. 396 – 401, Sanibel Island, Florida, USA.
  • [9] Nalepa, G., Kaczor, K. (2015). HQEd. Retrieved June 5, 2015, from http://ai.ia.agh.edu.pl/wiki/hekate:hqed.
  • [10] Ligęza, A., Nalepa, G. (2007). Knowledge Representation with Granular Attributive Logic for XTT-Based Expert Systems. In the Twentieth International Florida Artificial Intelligence Research Society Conference, 7-9 May 2007 530 – 535, Key West, Florida, USA.
  • [11] Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning. 1, 81 – 106.
  • [12] Ligęza, A., Nalepa, G. (2009). Rules verification and validation. In Giurca, A., Gašević, D., Taveter, K., (Eds.) Handbook of research on emerging rule-based languages and technologies: open solutions and approaches 273-301 Hershey, New York, IGI Global.
  • [13] Ligęza, A., Nalepa, G. (2005). Visual design and on-line verification of tabular rule-based systems with XTT. In Marktplatz Internet: Von e-Learning bis e-Payment: 13. Leipziger Informatik-Tage, LIT 2005, 21-23 September 2005 303-312. Bonn.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-fecf8ed2-b893-4ca5-a92b-fb296a139c32
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