Soft computing aims at using tricks or shortcuts that do not provide optimal solutions but useful approximations that can be computed at a reasonable cost. Such approximations often come in the form of heuristics and “rules of thumb.” Computer vision relies heavily on heuristics, being a simple example the detection of faces by detecting skin color. Another approach that may also be considered as heuristics is the use of inductive learning, where the idea is to emulate humans in the sense that achieving certain skills require gradual learning. Thus, we would not make an effort to articulate solutions as equations, rules or algorithms. The solution would instead be sought automatically by feeding the system with training examples that would allow it to classify new samples. This chapter describes two successful applications of such soft computing approaches in the field of human-computer interaction, showing how the clever use of heuristics and domain restrictions can help to find solutions for the most difficult problems in this field.
Soft Computing Techniques Applied to a Case Study of Air Quality in Industrial Areas in the Czech Republic