Applications of Intelligent Multiobjective Fuzzy Decision Making

Abstract

We discuss the major characteristics of two classes of fuzzy-logic techniques for the planning and control of systems operating in highly uncertain environments. These applications are characterized by strong requirements for robust behavior and for reactive response to unexpected circumstances. These requirements demand that the decision/control policies be capable of attaining, to the highest possible degree, a number of purposive and reactive goals. Fuzzy logic is an attractive approach to treat this type of questions because of its ability to combine numerical treatments of decision-making problems, its reliance on artificial-intelligence techniques for the context-dependent activation of control rules, and its conceptual relations to analogical-reasoning methods based on notions of similarity and resemblance.

Techniques in the first class — developed in the context of autonomous robot applications — are based on hierarchical supervisory approaches that divide decision/control responsibilities between low-level controllers — concerned with the attainment of specific goals — and high-level supervisors — deliberating about context-dependent goal attainability. Methods in the second class — developed for applications with stringent real-time requirements — are based on an axiomatic approach to the formal representation of knowledge about the relative importance of goals in various operational contexts.