Conclusion and Suggestions for Further Research
Our work has clearly revealed the roots of qualitative reasoning in AI in its attempts to formalize and model common-sense physical knowledge as well as human reasoning mechanisms. Since AI — as stated by [Newell 90] — provides a theoretical infrastructure for the study of human cognition, we may also conclude that qualitative reasoning aims at establishing a cognitive theory of “non-numerical” process description and at automating the phase of model building. And AI still constitutes the main background of qualitative reasoning. However, since qualitative reasoning deals with physical systems and their changes in time, basic concepts about dynamic systems such as state diagrams, trajectories, state variables or input — output relations have been introduced. Thus, simulation and system theory constitute a second basis of this approach. This does not seem to be surprising, as they both deal with the modeling of dynamic systems and the generation of their behavior. Nevertheless, although there exists an evident similarity between the semantics of both areas, the structural descriptions as well as the behavior generation mechanisms are derived from AI, based on mathematical concepts. And, as already stated, we can identify cognitive science as a further area close to qualitative reasoning. This is also shown by the discussion about the problem of causality. Because of shortcomings of the early developments — mainly the problem of ambiguity — further knowledge in the form of quantitative information and more elaborated reasoning techniques was integrated. However, these improvements were mainly based on well-known concepts of fields outside AI, as for example the non-crossing rules of trajectories or Markov chains, which we have introduced in this paper. Thus, we can identify qualitative reasoning as an interdisciplinary approach.
KeywordsSoftware Module Software Reusability Qualitative Reasoning Qualitative Simulation Software Object
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