Human reasoning usually is very approximate and involves various types of uncertainties.
Approximate reasoning is the computational modelling of any part of
the process used by humans to reason about natural phenomena or to solve real
world problems. The scope of this book includes fuzzy sets, Dempster-Shafer
theory, multi-valued logic, probability, random sets, and rough set, near set and
hybrid intelligent systems. Besides research articles and expository papers on theory
and algorithms of approximation reasoning, papers on numerical experiments
and real world applications were also encouraged. This Volume comprises of 12
chapters including an overview chapter providing an up-to-date and state-of-the
research on the applications of Computational Intelligence techniques for approximation
reasoning. The Volume is divided into 2 parts:
Part-I: Approximate Reasoning – Theoretical Foundations
Part-II: Approximate Reasoning – Success Stories and Real World Applications