Grammatical Inference

Grammatical inference focuses on automatically learning formal grammars from data, aiming to create computational models that accurately represent the underlying linguistic structure. Current research emphasizes developing efficient algorithms, such as genetic algorithms and SAT-based approaches, to infer models like Nondeterministic Finite Automata (NFAs) and regular grammars, often incorporating stochastic elements to handle probabilistic aspects of language. This field is crucial for advancing natural language processing, process discovery in areas like workflow analysis, and more broadly, for understanding and modeling complex systems through the lens of formal languages.