Fuzzy Contexts (Type C) and fuzzymorphism to solve situational discontinuity problems

Generalized solutions to complex problems often suffer from being overly complicated. The main contribution of this paper is to describe an architecture that allows for greater problem generalization without the traditional corresponding increase in complexity. The architecture extends traditional fuzzy logic and is called Fuzzy Contexts or Fuzzy Logic Type-C. Fuzzy logic permits partial membership and values can belong to multiple fuzzy sets. By breaking down a problem space into smaller contexts and allowing algorithms themselves to have relaxed memberships in those contexts, a Type-C solution can support multiple solutions to complex problems. This paper describes how problem spaces may be decomposed into smaller, more easily solvable components and fuzzified together under a Type-C hierarchy. Test results with a simulated robotic navigation system demonstrates how a Type-C implementation is able to improve upon a generalized fuzzy controller.