LP Heuristics over Conjunctions: Compilation, Convergence, Nogood Learning
Authors: Marcel Steinmetz, Joerg Hoffmann
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on IPC benchmarks show significant performance improvements in several domains. |
| Researcher Affiliation | Academia | Marcel Steinmetz and J org Hoffmann Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany {steinmetz,hoffmann}@cs.uni-saarland.de |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | Yes | We use the UIPC 16 benchmarks, as well as unsolvable resource-constrained (RCP) benchmarks [Nakhost et al., 2012; Steinmetz and Hoffmann, 2017]. |
| Dataset Splits | No | The paper uses standard benchmarks but does not specify explicit training/validation/test dataset splits, percentages, or sample counts. |
| Hardware Specification | Yes | All experiments were run on machines equipped with Intel Xeon E5-2660 CPUs, with runtime (memory) limits of 30 minutes (4 GB). |
| Software Dependencies | No | The paper mentions 'Fast Downward (FD) [Helmert, 2006]' as the implementation environment but does not specify version numbers for FD or any other software dependencies. |
| Experiment Setup | Yes | Similar to earlier works on the ΠC-compilation [Keyder et al., 2014], we cope with the worst-case explosion by imposing a size limit M on the ratio |AC|/|A|. Once ΠC reaches the limit M, we disable the generation of new conjunctions. We experimented with M {2, 4, 8, . . . , 1024, }, where for M = the size of ΠC is not limited. To counteract this brittleness, all our configurations in what follows combine the five variable orders, maintaining for each a separate conjunction set. |