Propagation complete encodings of smooth DNNF theories
This work provides a theoretical improvement for constraint satisfaction and automated reasoning, though it appears incremental as it builds directly on prior results.
The paper tackles the problem of encoding decomposable negation normal form (DNNF) theories into conjunctive normal form (CNF) by developing a propagation complete encoding, which ensures strong propagation across all variables, including auxiliary ones, unlike existing domain consistency encodings.
We investigate conjunctive normal form (CNF) encodings of a function represented with a decomposable negation normal form (DNNF). Several encodings of DNNFs and decision diagrams were considered by (Abio et al. 2016). The authors differentiate between encodings which implement consistency or domain consistency by unit propagation from encodings which are unit refutation complete or propagation complete. The difference is that in the former case we do not care about propagation strength of the encoding with respect to the auxiliary variables while in the latter case we treat all variables (the main and the auxiliary ones) in the same way. The currently known encodings of DNNF theories implement domain consistency. Building on these encodings we generalize the result of (Abio et al. 2016) on a propagation complete encoding of decision diagrams and present a propagation complete encoding of a DNNF and its generalization for variables with finite domains.