Managing Complexity in Qualitative Physics

物理系统的定性分析是人工智能的核心问题，但目前的技术仅对小系统的简单模型有效。指数时间复杂性和模糊结论的无处不在限制了定性物理推理技术在应用于大型模型时的有用性。对复杂系统的有效推理将需要系统的多个模型，这些模型包含适合不同任务的不同简化假设。因此，建议发展一种既有定性模型又有定性模型所基于的假设的理论，并通过构建推理程序来检验这一理论。该推理机将自动确定哪些假设是合适的，建立并使用相应的模型，最后检查其结论。这种方法应该会显著加快推理速度，减少歧义，并支持对更复杂的系统进行自动化分析。

The qualitative analysis of physical systems is a central problem in artificial intelligence, but current techniques are successful only for simple models of small systems. Exponential time complexity and the ubiquity of ambiguous conclusions limit the usefulness of qualitative physics reasoning techniques when applied to large models. Effective reasoning about complex systems will require multiple models of the system that embody different simplifying assumptions appropriate for different tasks. It is therefore proposed to develop a theory of both qualitative models and the assumption on which they are based, and to test that theory by constructing a reasoning program. This reasoner will automatically determine which assumptions are appropriate, build and use the corresponding model, and finally check its conclusions. This approach should significantly speed up reasoning, decrease ambiguity, and enable automated analysis of more complex systems.