A multimodel methodology for qualitative model engineering

Abstract

Qualitative models arising in artificial intelligence domain often concern real systems that are difficult to represent with traditional means. However, some promise for dealing with such systems is offered by research in simulation methodology. Such research produces models that combine both continuous and discrete-event formalisms. Nevertheless, the aims and approaches of the AI and the simulation communities remain rather mutually ill understood. Consequently, there is a need to bridge theory and methodology in order to have a uniform language when either analyzing or reasoning about physical systems. This article introduces a methodology and formalism for developing multiple, cooperative models of physical systems of the type studied in qualitative physics. The formalism combines discrete-event and continuous models and offers an approach to building intelligent machines capable of physical modeling and reasoning.