Inserire una breve descrizione delle modifiche fatte
(Richiesto)
Minor changes are by default collapsed in the page history.
No changes
The page does not exist yet.
Failed to load changes
Version by on
Leave Collaboration
Are you sure you want to leave the realtime collaboration and continue editing alone? The changes you save while editing alone will lead to merge conflicts with the changes auto-saved by the realtime editing session.
A Statistical Learning Method for Logic Programs with Distribution Semantics
Taisuke Sato
Leon Sterling (a cura di)
Logic Programming: Proceedings of the 12th International Conference on Logic Programming, pp. 715–729
Logic Programming
The MIT Press
giugno 1995
When a joint distribution PF is given to a set F of facts in a logic program DB = F ∪ R where R is a set of rules, we can further extend it to a joint distribution PDB over the set of possible least models of DB. We then define the semantics of DB with the associated distribution PF as PDB, and call it distribution semantics. While the distribution semantics is a straightforward generalization of the traditional least model semantics, it can capture semantics of diverse information processing systems ranging from Bayesian networks to Hidden Markov models to Boltzmann machines in a single framework with mathematical rigor. Thus symbolic computation and statistical modeling are integrated at semantic level. With this new semantics, we propose a statistical learning schema based on the EM algorithm known in statistics. It enables logic programs to learn from examples, and to adapt to the surrounding environment. We implement the schema for a subclass of logic programs called BS-programs.
The MIT Press Series in Logic Programming