Custom-Design of FDR Encodings: The Case of Red-Black Planning
Authors: Daniel Fišer, Daniel Gnad, Michael Katz, Jörg Hoffmann
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show that the performance of red-black planning can be improved through such FDR custom design. We implemented Algorithm 1 in C, used fam-groups inferred from PDDL [Fiˇser, 2020]2, and used the red-black heuristic h RB [Domshlak et al., 2015] implemented in FD [Helmert, 2006]. The experiments were performed on Intel(R) Xeon(R) Scalable Gold 6146 machines with CPLEX solver v12.6, and time and memory limits of 30min and 8Gi B. Table 1 shows the number of tasks solved (coverage) in the left, and the number of tasks solved by h RB in the initial state in the center. |
| Researcher Affiliation | Collaboration | Daniel Fiˇser1,2 , Daniel Gnad1 , Michael Katz3 and J org Hoffmann1 1Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany 2Czech Technical University in Prague, Faculty of Electrical Engineering, Czech Republic 3IBM Research, Yorktown Heights, NY, USA |
| Pseudocode | Yes | Algorithm 1: Inference of RSE-invertible mutex groups forming acyclic causal graph. |
| Open Source Code | Yes | We implemented Algorithm 1 in C, used fam-groups inferred from PDDL [Fiˇser, 2020]2...2https://gitlab.com/danfis/cpddl, branch ijcai21-fdr-red-black |
| Open Datasets | Yes | Out of all benchmarks from International Planning Competitions of 1998 to 2018 satisficing planning tracks, we selected all tasks where no conditional effects were created and at least one variable could be painted black. |
| Dataset Splits | No | The paper refers to planning tasks from international competitions but does not specify any explicit training, validation, or test dataset splits or percentages. |
| Hardware Specification | Yes | The experiments were performed on Intel(R) Xeon(R) Scalable Gold 6146 machines with CPLEX solver v12.6, and time and memory limits of 30min and 8Gi B. |
| Software Dependencies | Yes | CPLEX solver v12.6 |
| Experiment Setup | Yes | The experiments were performed on Intel(R) Xeon(R) Scalable Gold 6146 machines with CPLEX solver v12.6, and time and memory limits of 30min and 8Gi B. Out of all benchmarks from International Planning Competitions of 1998 to 2018 satisficing planning tracks, we selected all tasks where no conditional effects were created and at least one variable could be painted black. We compare the following configurations: the baseline configuration B runs the painting strategy denoted as A3 by Domshlak et al. [2015] on the default FDR encoding [Helmert, 2009]; M maximizes the number of black facts using Algorithm 1; C is the variant of Algorithm 1 using conflicts in a relaxed plan; and the oracle O picks the best FDR encoding and painting for each tested task. |