On State-Dominance Criteria in Fork-Decoupled Search
Authors: álvaro Torralba, Daniel Gnad, Patrick Dubbert, Jörg Hoffmann
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on IPC benchmarks attest to the possible practical benefits. We implemented our dominance pruning methods within the fork-decoupled search variant of FD [Helmert, 2006] by GH. Our baseline is GH s basic pruning B. For simplicity, we stick to the factoring strategy used by GH. This method greedily computes a factoring that maximizes the number of leaf factors. In case there are less than two leaves, the method abstains from solving a task. The rationale behind this is that the main advantage of decoupled search originates from not having to enumerate leaf state combinations across multiple leaf factors. Like GH, we show results on all IPC domains up to and including 2014 where the strategy does not abstain. |
| Researcher Affiliation | Academia | Alvaro Torralba, Daniel Gnad, Patrick Dubbert, and J org Hoffmann Saarland University Saarbr ucken, Germany {torralba, gnad, hoffmann}@cs.uni-saarland.de; s9padubb@stud.uni-saarland.de |
| Pseudocode | No | The paper describes algorithms and logic in prose and mathematical notation but does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | Experiments on International Planning Competition (IPC) benchmarks attest to the possible practical benefits. Like GH, we show results on all IPC domains up to and including 2014 where the strategy does not abstain. |
| Dataset Splits | No | The paper refers to using IPC benchmarks but does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts. It evaluates performance metrics like 'coverage data' and 'expansions' on the benchmark instances. |
| Hardware Specification | Yes | All experiments are run on a cluster of Intel E5-2660 machines running at 2.20 GHz, with time (memory) cut-offs of 30 minutes (4 GB). |
| Software Dependencies | No | We implemented our dominance pruning methods within the fork-decoupled search variant of FD [Helmert, 2006] by GH. We run a blind heuristic to identify the influence of different pruning methods per se, and we run LM-cut [Helmert and Domshlak, 2009] as a stateof-the-art heuristic. The paper mentions software tools and heuristics but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | All experiments are run on a cluster of Intel E5-2660 machines running at 2.20 GHz, with time (memory) cut-offs of 30 minutes (4 GB). We focus on optimal planning, the main purpose of optimality-preserving pruning. We run a blind heuristic to identify the influence of different pruning methods per se, and we run LM-cut [Helmert and Domshlak, 2009] as a stateof-the-art heuristic. GH introduced two decoupled variants of A , Fork-Decoupled A and Anytime Fork-Root A , which to simplify terminology we will refer to as Decoupled A (DA ) and Anytime Decoupled A (ADA ). |