Model decomposition and simulation: a component based qualitative simulation algorithm

  • Authors:

  • Daniel J. Clancy;Benjamin Kuipers

  • Affiliations:

  • Department of Computer Sciences, University of Texas at Austin, Austin, Texas;Department of Computer Sciences, University of Texas at Austin, Austin, Texas

  • Venue:
  • AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
  • Year:
  • 1997

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Abstract

Traditionally, qualitative simulation uses a global, state-based representation to describe the behavior of the modeled system. For larger, more complex systems this representation proves extremely inefficient since it provides a complete temporal ordering of all potential distinctions leading to a large, complex behavioral description that obscures relevant distinctions, or even fails to terminate. The model decomposition and simulation algorithm (DecSIM) uses a divide and conquer approach to qualitative simulation. Variables within the system are partitioned into components. Each component is viewed as a separate system and is simulated using a state-based representation limited to the variables within the component. Interactions between components are reasoned about separately. DecSIM provides a promising paradigm for qualitative simulation whose complexity is driven by the complexity of the problem specification rather than the inference mechanism used.