| Full text | |
| Author(s): |
Cozman, Fabio G.
;
Maua, Denis D.
;
Antonucci, A
;
Cholvy, L
;
Papini, O
Total Authors: 5
|
| Document type: | Journal article |
| Source: | SYMBOLIC AND QUANTITATIVE APPROACHES TO REASONING WITH UNCERTAINTY, ECSQARU 2017; v. 10369, p. 10-pg., 2017-01-01. |
| Abstract | |
A popular family of probabilistic logic programming languages combines logic programs with independent probabilistic facts. We study the complexity of marginal inference, most probable explanations, and maximum a posteriori calculations for propositional/relational probabilistic logic programs that are acyclic/definite/stratified/normal/disjunctive. We show that complexity classes Sigma(k) and PP Sigma k (for various values of k) and NPPP are all reached by such computations. (AU) | |
| FAPESP's process: | 16/01055-1 - Learning of Tractable Probabilistic Models with Application to Multilabel Classification |
| Grantee: | Denis Deratani Mauá |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 15/21880-4 - PROVERBS -- PRobabilistic OVERconstrained Boolean Systems: reasoning tools and applications |
| Grantee: | Marcelo Finger |
| Support Opportunities: | Regular Research Grants |