Managing Complexity in Qualitative Physics

物理系统的定性分析是一个中心问题 但目前的技术是成功的， 仅适用于小型系统的简单模型。 指数时间 复杂性和普遍存在的模棱两可的结论限制了 定性物理推理技术的实用性， 适用于大型模型。 关于复杂性的有效推理 系统将需要多种系统模型， 不同的简化假设适用于不同的 任务 因此，有人建议发展一种理论， 定性模型及其所依据的假设， 并通过构建一个推理程序来验证这个理论。 该推理器将自动确定哪些假设 适当的，建立和使用相应的模型，最后 检查其结论。 这种方法应该大大加快 提高推理能力，减少歧义，实现自动分析 更复杂的系统。

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.