• DocumentCode
    1923411
  • Title

    Uml-Based Representational Re-Description of Concept Development

  • Author

    Wei, Hui ; He, Wen ; Chen, Yan

  • Author_Institution
    Fudan Univ., Shanghai
  • Volume
    1
  • fYear
    2007
  • fDate
    19-22 Aug. 2007
  • Firstpage
    131
  • Lastpage
    139
  • Abstract
    Knowledge-based problem solving requires a conceptual system that is comparatively rich and complete, especially when the problem is domain unrestrictive. The methods about knowledge acquisition, representation and usage in classical Knowledge Engineering can only adapt themselves to domain restricted problems. This is because it doesn´t take a developmental view to construct conceptual system and consequently it is confronted with Framework Problem. In Cognitive Psychology, the study of conceptual system has an in-depth cognitive investigate on issues of development and representation. However, there lack investigations on the details of system construction and realization. Based on the theory of Developmental Psychology, this paper proposes an object-based representation method for conceptual system, focusing on the representation and development of concepts on four levels: Implicit (I), Explicit 1 (E1), Explicit 2 (E2) and Explicit 3 (E3) representations. It will contribute well to the adaptability and flexibility in the reasoning and problem solving of knowledge-based systems.
  • Keywords
    Unified Modeling Language; knowledge acquisition; knowledge representation; UML-based representational redescription; concept development; framework problem; knowledge acquisition; knowledge representation; knowledge-based problem solving; Artificial intelligence; Cognition; Cybernetics; Knowledge based systems; Knowledge engineering; Knowledge representation; Machine learning; Machine learning algorithms; Problem-solving; Psychology; Artificial intelligence; Concept acquisition; Learning; Representational re-description;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2007 International Conference on
  • Conference_Location
    Hong Kong
  • Print_ISBN
    978-1-4244-0973-0
  • Electronic_ISBN
    978-1-4244-0973-0
  • Type

    conf

  • DOI
    10.1109/ICMLC.2007.4370128
  • Filename
    4370128