NYI: Using Fuzzy Logic to Deal with Qualitive Requirements and Uncertaintly in the Environment

智能系统通常需要处理两种类型的 不确定性：（1）系统要求是定性的， （2）外部环境的不确定性 环境本研究的主要目的是开发 处理这些问题的合理和实用的技术。到 解决第一个问题，基于模糊逻辑的方法， 指定和验证质量要求正在 开发解释性地捕捉系统的弹性 需求有助于探索各种权衡 在设计阶段，并使更现实的验证， 实施的制度。为了解决第二个问题，系统 设计混合自主智能的建模技术 系统正在开发中。这些技术使用模糊逻辑来 将人工智能符号问题求解与数值处理相结合 神经网络和基于模型的控制。潜在 这种混合系统的工业应用范围从 自动车辆系统的石化过程控制， 自动化制造系统。方法和技术 在这个研究项目中开发的不仅将提高 质量和下一代智能的适应性 系统，但也将降低设计和 维护它们。

Intelligent systems often need to deal with two kinds of uncertainty: (1) system requirements that are qualitative in nature, and (2) uncertainty about the state of the external environment. The primary objective of this research is to develop sound and practical techniques for dealing with these issues. To address the first issue, fuzzy logic based methodologies for specifying and validating qualitative requirements are being developed. Explicitly capturing the elasticity of the system's requirements facilitates the exploration of various trade-offs during the design stage and enables a more realistic validation of the implemented system. To address the second issue, systematic modeling techniques for designing hybrid autonomous intelligent systems are being developed. These techniques use fuzzy logic to integrate AI symbolic problem solving with the numeric processing exhibited by neural networks and model-based control. Potential industrial applications of such hybrid systems range from the petrochemical process control to autonomous vehicle systems and automated manufacturing systems. Methodologies and techniques developed in this research project will not only enhance the quality and the adaptability of the next generation of intelligent systems, but will also reduce the cost for designing and maintaining them.