Min-based possibilistic networks are important graphical models for representing and analyzing uncertain information using the possibility theory framework. Diverse inference methods were developed for efficient computations in these models, we cite in particular, compilation-based inference, which consists in encoding the network into a CNF base and compiling this latter to efficiently compute the impact of an evidence on variables. This paper emphasizes on an experimental study between several compilation-based inference approaches in terms of CNF parameters, compiled bases parameters and inference time. The behavior of compiled bases is studied in depth for both of local structure and possibilistic local structure strategies.
展开▼
