Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Logical Credal Networks or LCNs is a recent probabilistic logic designed for effective aggregation and reasoning over multiple sources of imprecise knowledge. An LCN specifies a set of probability distributions over all interpretations of a set of logical formulas for which marginal and conditional probability bounds on their truth values are known. Inference in LCNs involves the exact solution of a non-convex non-linear program defined over an exponentially large number of non-negative real valued variables and, therefore, is limited to relatively small problems. In this paper, we present ARIEL - a novel iterative message-passing scheme for approximate inference in LCNs. Inspired by classical belief propagation for graphical models, our method propagates messages that involve solving considerably smaller local non-linear programs. Experiments on several classes of LCNs demonstrate clearly that ARIEL yields high quality solutions compared with exact inference and scales to much larger problems than previously considered.
Gabriele Picco, Lam Thanh Hoang, et al.
EMNLP 2021
Elliot Nelson, Debarun Bhattacharjya, et al.
UAI 2022
Haifeng Qian, Sani R. Nassif, et al.
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Thabang Lebese, Ndivhuwo Makondo, et al.
NeurIPS 2021