Applicability of Crisp and Fuzzy Logic in Intelligent Response Generation
This work addresses decision-making in intelligent systems for humans and robots, but it is incremental as it reviews existing logic types without introducing new methods.
The paper compares crisp and fuzzy logic for intelligent response generation, concluding that crisp logic achieves perfect handling with complete knowledge, while fuzzy logic is more effective with incomplete knowledge but yields imperfect results.
This paper discusses the merits and demerits of crisp logic and fuzzy logic with respect to their applicability in intelligent response generation by a human being and by a robot. Intelligent systems must have the capability of taking decisions that are wise and handle situations intelligently. A direct relationship exists between the level of perfection in handling a situation and the level of completeness of the available knowledge or information or data required to handle the situation. The paper concludes that the use of crisp logic with complete knowledge leads to perfection in handling situations whereas fuzzy logic can handle situations imperfectly only. However, in the light of availability of incomplete knowledge fuzzy theory is more effective but may be disadvantageous as compared to crisp logic.