Abstract
Computational Intelligence (CI) is commonly regarded as a synergistic framework within which one can analyze and design (synthesize) intelligent systems. The methodology of CI has been firmly established through the unified and highly collaborative usage of the underlying information technologies of fuzzy sets (and granular computing, in general), neural networks, and evolutionary optimization. It is the collaboration which makes the CI models highly versatile, computationally attractive and very much user-oriented. While this facet of functional collaboration between these three key information technologies has been broadly recognized and acknowledged within the research community, there is also another setting where the collaboration aspects start to play an important role. They are inherently associated with the nature of intelligent systems that quite often become distributed and whose interactions come with a suite of various mechanisms of collaboration. In this study, we focus on collaborative Computational Intelligence which dwells upon numerous forms of collaborative linkages in distributed intelligent systems. In the context of intelligent systems we are usually faced with various sources of data in terms of their quality, granularity and origin. We may encounter large quantities of numeric data coming from noisy sensors, linguistic findings conveyed by rules and associations and perceptions offered by human observers. Given the enormous diversity of the sources of data and knowledge the ensuing quality of data deserves careful attention. Knowledge reuse and knowledge sharing have been playing a vital role in the knowledge management which has become amplified over the time we encounter information systems of increasing complexity and of a distributed nature. The collaborative CI is aimed at the effective exchange of locally available knowledge. The exchange is commonly accomplished by interacting at the level of information granules rather than numeric quantitative. As a detailed case study we discuss experience–consistent modeling of CI constructs and raise an issue of knowledge reuse in the setting of constructs of Computational Intelligence. One could note that the knowledge-based component (viz. previously built CI constructs) can serve as a certain form of the regularization mechanism encountered quite often in various modeling platforms. The optimization procedure applied there helps us strike a sound balance between the data-driven and knowledge-driven evidence. We introduce a general scheme of optimization and show an effective way of reusing knowledge. In the sequel, we demonstrate the development details with regard to fuzzy rule-based models and neural networks.