Toward rough set based insightful reasoning in intelligent systems

Abstract

This paper explores a rough set-based approach for supporting insightful reasoning in Intelligent Systems (ISs). The novelty lies in the introduction of a new concept for approximate reasoning processes based on granular computations. Although many rough set theory extensions developed over time focus on reasoning about (partial) set inclusion, these approximation spaces sometimes fall short when dealing with crucial aspects of approximate reasoning within ISs. Specifically, these systems aim to construct high-quality approximations of compound decision granules that represent solutions. Here, we present the basis for insightful reasoning realized through approximate reasoning processes grounded in granular computations. By doing so, we provide a sufficiently rich basis for designing IS problem solvers. This basis allows ISs to restructure or adapt their reasoning based on the generated granular computations, ultimately leading to high-quality granular solutions.