Index Grammars, Music, and Formal Languages This series of articles explores an unusual territory: where formal language theory meets algorithmic music. The common thread is BP2SC, a transpiler that converts musical grammars from the Bol Processor into SuperCollider patterns. Each article is designed to be accessible without academic prerequisites, yet rigorous enough to serve as […]
Explore a groundbreaking thesis on grammar learning. The economy of laziness and the ecological oracle, driving prediction error, reveal a unique process. Click now!
**Grammatical inference**: the ultimate challenge. Impossible (Gold), hard as cracking RSA, it is the extreme case of recognition. Learning is compressing.
The P-chain revolutionizes psycholinguistics: your brain predicts by producing to understand. Prediction error drives learning. Discover this unified model!
The slogan ‘generating is easy, analyzing is hard’ is false. Semantic-constrained generation can be exponential or NP-complete, even surpassing parsing.
The **complexity matrix** dissects generation-recognition asymmetry. Explore 6 sub-problems, differential coupling, and sign reversal. Essential reading!
Despite 50 years, bidirectionality in reversible grammars remains rare. Discover why asymmetry is structural and requires a declarative approach in NLP.
Why is generating simpler than analyzing? Discover the formulas of generation-parsing asymmetry: complexity, Catalan numbers, and surprisal.
Generation is always top-down. Parsing (LL, LR) has a unique directional freedom. Discover this fundamental asymmetry, essential for compilers.
Discover the duality of formal grammars: why generating and analyzing are not symmetrical. A key challenge at the intersection of computer science, AI, and music.