Weighted Finite

Weighted finite automata (WFAs) are computational models extending finite automata by assigning weights to transitions, enabling representation of more complex sequential data and probabilistic processes. Current research focuses on efficient algorithms for WFA learning, inference, and application in diverse areas like explainable AI (using SHAP values), natural language processing (e.g., improving transformer models and speech recognition), and computational biology (modeling immune systems). The ability of WFAs to compactly represent complex systems, coupled with advancements in efficient algorithms, makes them a valuable tool for both theoretical analysis and practical applications across multiple scientific domains.