Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoning

This paper enhances Logic Tensor Networks through the integration of uninorm based fuzzy operators. Uninorms, a class of operators that bridge the gap between t-norms and t-conorms, offer unparalleled flexibility and adaptability, making them ideal for modeling the complex, often ambiguous relationships inherent in real-world data. By embedding these operators into Logic Tensor Networks, we present a methodology that significantly increases the network’s capability to handle nuanced logical operations, thereby improving its applicability across different domains. Through a series of experiments, we demonstrate the efficacy of uninorm based operators in enhancing the precision of Logic Tensor Networks. Our findings suggest that the inclusion of uninorms not only broadens the scope of problems that Logic Tensor Networks can address but also deepens their reasoning capabilities, paving the way for more sophisticated artificial intelligence systems. This work lays a foundational stone for future research in the intersection of fuzzy logic and neural-symbolic computing, suggesting directions for further exploration and integration of fuzzy systems elements into Logic Tensor Networks