Qualitative Modeling and Simulation of Physical Systems

定性建模和仿真方法提供了表达机制结构和初始状态的不完全知识的能力，并得出对其可能行为的有用预测。这个能力对于模型来说是非常重要的。基于推理的诊断、设计和决策。提出的工作建立在研究者之前开发的QSIM表示和定性模拟算法的基础上。在模型中。在模拟领域，新的工作集中在这些问题上：(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)分层结构方法，如按相对时间对机制进行抽象。规模，以处理复杂的机制。在模型中。在构建领域，调查涉及到构建定性模型的自动方法，主要关注两个问题：(1)从潜在的大量切线相关描述库中选择一小组描述以合并到有用的模型中；(2)利用模型的局限性进行推理，知道什么时候是有用的，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.