On compositional modelling

  • Authors:

  • Jeroen Keppens;Qiang Shen

  • Affiliations:

  • School of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland. jeroen@dai.ed.ac.uk;School of Artificial Intelligence, University of Edinburgh, Edinburgh, Scotland. qiangs@dai.ed.ac.uk

  • Venue:
  • The Knowledge Engineering Review
  • Year:
  • 2001

Quantified Score

Hi-index 0.00

Visualization

Abstract

Many solutions to AI problems require the task to be represented in one of a multitude of rigorous mathematical formalisms. The construction of such mathematical models forms a difficult problem which is often left to the user of the problem-solver. This void between problem-solvers and their problems is studied by the eclectic field of automated modelling. Within this field, compositional modelling, a knowledge-based methodology for system-modelling, has established itself as a leading approach. In general, a compositional modeller organises knowledge in a structure of composable fragments that relate to particular system components or processes. Its embedded inference mechanism chooses the appropriate fragments with respect to a given problem, instantiates and assembles them into a consistent system model. Many different types of compositional modeller exist, however, with significant differences in their knowledge representation and approach to inference. This paper examines compositional modelling. It presents a general framework for building and analysing compositional modellers. Based on this framework, a number of influential compositional modellers are examined and compared. The paper also identifies the strengths and weaknesses of compositional modelling and discusses some typical applications.