Soft computing (SC) solutions are unpredictable, uncertain and between 0 and 1. Soft Computing became a formal area of study in Computer Science in the early 1990s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, humanities and similar fields often remained intractable to conventional mathematical and analytical methods. However, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. Soft computing deals with imprecision, uncertainty, partial truth, and approximation to achieve practicability, robustness and low solution cost. As such it forms the basis of a considerable amount of machine learning techniques. Recent trends tend to involve evolutionary and swarm intelligence based algorithms and bio-inspired computation.

Aims and Scope:
1. Algebra and algebraic logic
2. Computat√ional paradigms and computational complexity
3. Description logic, temporal logic, dynamic logic, and modal logic
4. Domain theory and type theory
5. Fuzzy logic, fuzzy set theory, and many-valued logic
6. Substructural logic
7. Probability logic, belief functions, etc.