An Improved Decision-DNNF Compiler

Authors: Jean-Marie Lagniez, Pierre Marquis

IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental In order to assess D4 and to compare it with C2D and Dsharp, we performed experiments on many benchmarks, coming from several families. The results obtained clearly show the benefits offered by D4.
Researcher Affiliation Academia Jean-Marie Lagniez and Pierre Marquis CRIL, U. Artois & CNRS, F-62300 Lens, France, e-mail: {lagniez, marquis}@cril.univ-artois.fr
Pseudocode Yes The pseudo-code of D4. Algorithm ?? provides the pseudocode of the compiler D4. The compilation of a given CNF formula Σ is achieved by calling D4(Σ, ).
Open Source Code Yes The runtime code of D4, the benchmarks used, and some additional empirical results are available on line from https: //www.cril.univ-artois.fr/KC/.
Open Datasets Yes We have considered 703 CNF instances from the SAT LIBrary www.cs.ubc.ca/ hoos/SATLIB/index-ubc. html, gathered into 8 data sets, as follows: BN (Bayesian networks) (192), BMC (Bounded Model Checking) (18), Circuit (41), Configuration (35), Handmade (58), Planning (248), Random (104), Qif (7) (Quantitative Information Flow analysis security).
Dataset Splits No No specific training/test/validation splits are mentioned. The experiments are conducted on 703 CNF instances from the SAT LIBrary.
Hardware Specification Yes The experiments have been conducted on Intel Xeon E5-2643 (3.30 GHz) processors with 32 Gi B RAM on Linux Cent OS.
Software Dependencies Yes We used our own solver solve, which is based on the state-of-the-art SAT solver Mini SAT 2.2 [E en and S orensson, 2003].
Experiment Setup Yes A time-out of 1h and a memory-out of 7.6 Gi B has been considered for each instance.