The Promising Future of Fuzzy Logic

As Charles Elkan points out in his article, the foundation of fuzzy logic is the notion of partial truth and degrees of truth in any proposition stating facts about real-world objects, whether these objects are entities, events, relations, algorithms, systems, or machines. Facts and propositions are uncertain, ambiguous, and incomplete and more importantly, they are goal-oriented, intentional, and subjective to the observer’s perceptual capabilities, mental constructs, and meaning systems. In this philosophical view, the universe is seen as holistic, dynamic, and chaotic. Fuzzy logic is basically a theory of human perception and cognition. It is concerned with the marvelous paradigm and methodology discovered by evolution and realized in our brains to cope with complexity, holism, dynamism, and chaos in the world around us. The goal of a fuzzy expert system is to take in subjective, partially true facts that are randomly distributed over a sample space, and build a knowledge-based expert system that will apply certain reasoning and aggregation strategies to make useful decisions. These decisions are again approximate, and have partial degrees of truth and likelihood; the decisions and derived facts are reliable to the best of our available knowledge. The important fact about these systems is that decisions made by them can be iteratively and adaptively improved, and as more such random/fuzzy facts accumulate, the results will converge to real precise facts. In this view of reality, no proposition is always 100% true for 100% of the observers and experts. Absolute certainty, absolute truth, and absolute objectivity are impossible because they require infinite pieces of information, infinite number of samples, and infinitely many observers. Fuzzy logic-based models are actually efforts in building our perceptual models and maps of reality, and not the reality itself.