Fuzzy Interpolation Systems and Applications

Fuzzy inference systems provide a simple yet effective solution to complex non-linear problems, which have been applied to numerous real-world applications with great success. However, conventional fuzzy inference systems may suffer from either too sparse, too complex or imbalanced rule bases, given that the data may be unevenly distributed in the problem space regardless of its volume. Fuzzy interpolation addresses this. It enables fuzzy inferences with sparse rule bases when the sparse rule base does not cover a given input, and it simplifies very dense rule bases by approximating certain rules with their neighbouring ones. This chapter systematically reviews different types of fuzzy interpolation approaches and their variations, in terms of both the interpolation mechanism (inference engine) and sparse rule base generation. Representative applications of fuzzy interpolation in the field of control are also revisited in this chapter, which not only validate fuzzy interpolation approaches but also demonstrate its efficacy and potential for wider applications

The Geometric Mean as a Generator of Truth-Value in Heuristic Expert Systems: An Improvement over the Fuzzy Weighted Arithmetic Mean

Many earlier expert systems that were modeled after MYCIN, the first expert system, employed truth-value factors for their rule antecedents (premises) and consequents (conclusions). These crisp truth-value factors were usually called certainty factors and attempted to provide a measure of confidence and computational capability to the analysis of rule uncertainty (Shortliffe, 1977; Kandel, 1994). However, in the literature criticism has been often expressed concerning the lack of precision a crisp truth/certainty factor value conveys (Zadeh, 1983; Turban, 1993). Zadeh (1973) and Xingui (1988) utilized the weighted fuzzy average algorithm to improve the precision of truth/certainty factor values. Kandel (1994) further extended the fuzzy weighted mean concept introducing rule confidence, priority, and conclusion weighting factors. Later, Chen (1996) further modified the fuzzy weighted mean algorithm through the factoring of independent rule premise and consequent weights, truth-values and certainty factors. All of these progressive variants of the fuzzy weighted mean enhanced perceived rule antecedent and consequent truth-value. This research investigated a modification of the fuzzy weighted algorithms of Chen and Kandel utilized in assessing heuristic expert system rule truth-value. Their algorithms were modified to demonstrate that a more statistically precise rule truth-value can be achieved by utilizing the geometric mean to aggregate rule truth-value components

Paradigm Shift from Vague Legal Contracts to Blockchain-Based Smart Contracts

In this dissertation, we address the problem of vagueness in traditional legal contracts by presenting novel methodologies that aid in the paradigm shift from traditional legal contracts to smart contracts. We discuss key enabling technologies that assist in converting the traditional natural language legal contract, which is full of vague words, phrases, and sentences to the blockchain-based precise smart contract, including metrics evaluation during our conversion experiment. To address the challenge of this contract-transformation process, we propose four novel proof-of-concept approaches that take vagueness and different possible interpretations into significant consideration, where we experiment with popular vendors' existing vague legal contracts. We show through experiments that our proposed methodologies are able to study the degree of vagueness in every interpretation and demonstrate which vendor's translated-smart contract can be more accurate, optimized, and have a lesser degree of vagueness. We also incorporated the method of fuzzy logic inside the blockchain-based smart contract, to successfully model the semantics of linguistic expressions. Our experiments and results show that the smart contract with the higher degrees of truth can be very complex technically but more accurate at the same time. By using fuzzy logic inside a smart contract, it becomes easier to solve the problem of contractual ambiguities as well as expedite the process of claiming compensation when implemented in a blockchain-based smart contract