OMOS(ID:4191/omo001)

for Operational Models of Problem-Solving

KADS Knowledge modelling language

German National Research Center for Computer Science, GMD, in Bonn.

Related languages
OMOS => MoMo   Evolution of
OMOS => OMOS   Written using
References:
  • Linster, M. ; W. Karbach, A. Voß, and J. Walther: An Analysis of the Role of Operational Modelling Languages in the Development of Knowledge-Based Systems, Proceedings of the 2nd Japanese Knowledge Acquisition for Knowledge-Based Systems Workshop (JKAW´1992) , Hatoyama, Japan, November 9-13, 1992. view details
  • Linster, M. Knowledge Acquisition Based on Explicit Methods of Problem Solving, Ph. D, thesis, University of Kaiserslautern, February 1992. view details
  • Linster, M. Linking Modeling to Make Sense and Modeling to Implement Systems in an Operational Modeling Environment. In Proceedings of the 6th European Knowledge Acquisition for Knowledge-Based Systems Workshop (EKAW-92), May 18-22, Heidelberg/Kaiserslautern, 1992, T. Wetter et al. (eds.), Current Developments in Knowledge Acquisition, Lecture Notes in Artificial Intelligence, no 599, Springer-Verlag, Berlin, 1992. view details
  • Linster, M. Tackling the Office-Plan Problem with OMOS. in M. Linster (ed.): Sisyphus ´91: Models of Problem Solving, Arbeitspapiere der GMD, no 630, March 1992. view details
  • Linster, M. Using the Operational Modelling Language OMOS to Tackle the Sisyphus´92 Office-Planning Problem. In. Language Comparison 48 in Linster, M. (ed.): Sisyphus ´92: Models of Problem Solving, Arbeitspapiere der GMD, no 663, July 1992. view details
  • Linster, M. Using OMOS to Represent KADS Conceptual Models. In G. Schreiber, B. Wielinga, and J. Breuka (eds.): KADS. A Principled Approach to Knowledge-Based System Development, Knowled-Based Systems, vol 11, Academic Press, London, 1993. view details
  • Fensel, Dieter and van Harmelen, Frank "A Comparison of Languages which Operationalise and Formalise KADS Models of Expertise" The Knowledge Engineering Review, 9(2), 1994. view details Abstract: In the field of Knowledge Engineering, dissatisfaction with  the rapid-prototyping approach has led to a number of more principled  methodologies for the construction of knowledgebased systems. Instead of  immediately implementing the gathered and interpreted knowledge in a  given implementation formalism according to the rapid-prototyping  approach, many such methodologies centre around the notion of a  conceptual model: an abstract, implementation independent description of  the relevant problem solving expertise. A conceptual model should  describe the task which is solved by the system and the knowledge which  is required by it. Although such conceptual models have often been  formulated in an informal way, recent years have seen the advent of  formal and operational languages to describe such conceptual models more  precisely, and operationally as a means for model evaluation. In this  paper, we study a number of such formal and operational languages for  specifying conceptual models. In order to enable a meaningful comparison  of such languages, we focus on languages which are all aimed at the same  underlying conceptual model, namely that from the KADS method for  building KBS. We describe eight formal languages for KADS models of  expertise, and compare these languages with respect to their modelling  primitives, their semantics, their implementations and their  applications. Future research issues in the area of formal and  operational specification languages for KBS are identified as the result  of studying these languages. The paper also contains an extensive  bibliography of research in this area.


    External link: Online copy Extract: FORKADS
    FORKADS has been developed at the IBM Germany Scientific Center in Heidelberg. It was one of the first published approaches to formal KADS models.
    Contrary to the languages described so far, FORKADS aims not only at operationalizing KADS models, but also at giving formal foundations to KADS models. Furthermore, the aim is to use this language as a (or perhaps even the only) communication medium between the people responsible for knowledge acquisition and those responsible for system design. For this purpose, the main foundation of FORKADS is a first-order logical language which is extended with notions of concept heterarchies and procedural attachment. In many respects FORKADS is rooted in the LLILOG language Extract: (ML)2
    (ML)2  is a language for formalizing KADS models of expertise. It was developed in the course of the ESPRIT Projects "REFLECT" and "KADS-II" and a bilateral research project of the Netherlands Energy Research Foundation ECN and the University of Amsterdam. Although a subset of (ML)2 can be operationalized to
    allow explorative prototyping it is mainly introduced as a formalization language. Extract: OMOS
    Operational Models of Problem-Solving (OMOS) is a language to operationalize KADS models of expertise. It was developed at the German National Research Center for Computer Science, GMD, in Bonn.
    The main point of OMOS is to extend the Role-Limiting Methods [Mar88] or Method-to-Task approach [Mus89] by integrating them into a KADS framework. The Role-Limiting Methods approach provides shells with corresponding knowledge acquisition tools like MOLE, MORE or SALT [Mar88]. The shells contain a fixed data structure and an algorithm which works on it. The knowledge acquisition process consists of two activities. First, a proper shell must be chosen, and second, this shell must be filled with cases, i.e. with assertional knowledge.
    The approach is not meaningful if no previously developed shell fits the chosen task.
    Therefore, OMOS allows a bottom-up development of such problem-solving methods and provides some tools which support the acquisition of assertional knowledge. A problemsolving process can be modelled by using role and value changes of given instances. Besides extending existing expert shell approaches OMOS is based on the KADS model of expertise and therefore makes it possible to evaluate such models using explorative prototyping.