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.
