Qualitative Modeling and Simulation of Physical Systems

定性建模和仿真方法能够表达机构的结构和初始状态的不完全知识，并对其可能的行为得出有用的预测。这种能力对于基于模型的推理在诊断、设计和决策中具有重要意义。建议的工作建立在研究人员先前开发的定性模拟的QSIM表示和算法的基础上。在模型仿真领域，新的工作集中在以下问题上：(1)用定性预测来解释定量数据：(2)将定性行为表示为定性相空间中的轨迹，从而允许应用更强大的数学方法；(3)层次结构方法，如按相对时间尺度抽象机制，以处理复杂机制。在模型构建领域，研究涉及建立定性模型的自动方法，主要集中在两个主要问题上：(1)从潜在的巨大的切线相关描述库中选择一小部分描述纳入到有用的模型中；(2)用模型的局限性进行推理，知道它何时有用，并结合多个模型的结论。

Qualitative modeling and simulation methods provide the ability to express incomplete knowledge of the structure and initial state of a mechanism, and to derive useful predictions about its possible behaviors. This capability is of major significance for model.based reasoning in diagnosis, design, and decision.making. The proposed work builds on the investigator's prior development of the QSIM representation and algorithm for qualitative simulation. In the model.simulation area, the new work focuses on these problems: (1) the interpretation of quantitative data in terms of qualitative predictions; (2) representing qualitative behaviors as trajectories in a qualitative phase space, allowing application of more powerful mathematical methods; and (3) hierarchical structuring methods, such as abstraction of mechanisms by relative time.scale, to deal with complex mechanisms. In the model.building area, the investigation concerns automatic methods for building qualitative models, focusing on two major issues: (1) the selection, from potentially vast library of tangentially relevant descriptions, of a small set of descriptions to incorporate into a useful model; and (2) reasoning with the limits of the model, to know when it is useful, and combining conclusions from multiple models.