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In the current chapter we provide a small case study, based on the Witas Uav application domain to illustrate various knowledge representation and reasoning techniques presented in the book.

Solving complex problems by intelligent systems, in such areas as identification of objects by autonomous systems, web mining or sensor fusion, requires techniques for combining information from many different sources with different degrees of quality. Usually, the information is inaccurate and incomplete. One paradigm for dealing with such complex problems is granular computing.

In traditional approaches to knowledge representation, notions such as tolerance measures on data, distance between objects or individuals, and similarity measures between primitive and complex data structures such as properties and relations, elementary and complex descriptors, decision rules, information systems, and relational databases, are rarely considered. This is unfortunate because many complex systems which have knowledge representation components receive and process data which is incomplete, noisy, and uncertain. There is often a need to use tolerance and similarity measures in processes of data and knowledge abstraction. This is a particular problem in the area of cognitive robotics where data input by sensors has to be fused, filtered and integrated with more traditional qualitative knowledge structures. A great many levels of knowledge abstraction and data reduction must be used as one tries to integrate newly acquired data with existing data which has previously been abstracted and represented implicitly in the form of more qualitative data and knowledge structures.

This chapter is primarily devoted to a rough set methodology for .

In his chapter, the process of inducing classifiers from actual data is discussed. This is done using a case study roughly related to the example considered in Section 14.7. A classifier will be constructed for the concept . Recall that this concept is intended to represent potentially dangerous traffic situations.