Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Encoding Domain Transitions for Constraint-Based Planning
Authors: Nina Ghanbari Ghooshchi, Majid Namazi, M.A.Hakim Newton, Abdul Sattar
JAIR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP s efficiency over the original Pa P2 running on SICStus Prolog and our reconstructed and enhanced versions of Pa P2 running on Minion. [...] 7. Experimental Results. We ran all experiments reported in this paper on the same high performance computing cluster Gowonda at Griffith University. |
| Researcher Affiliation | Collaboration | Nina Ghanbari Ghooshchi EMAIL Institute for Integrated and Intelligent Systems (IIIS) Griffith University, Brisbane, Australia Data61, CSIRO, Australia |
| Pseudocode | Yes | Algorithm 1 Construction of Redefined DTGs; Algorithm 2 A Non-Deterministic Search for Parallel Planning; Algorithm 3 DTGs To CSP Encoding; Algorithm 4 Transition constraint tc(vτ) using a table; Algorithm 5 Adding Parallelism Variables to Transition Constraints; Algorithm 6 Decoding CSP Solution to Plan; Algorithm 7 FSA To CSP Encoding; Algorithm 8 Action Succession Constraint asc(vτ, vτ+1) using a table; Algorithm 9 Synchronisation Constraint sc(vτ) using a table; Algorithm 10 Mutex Constraint mc(v, k, v, k, τ) using a negative table |
| Open Source Code | No | The paper does not contain any explicit statement about providing source code, a link to a repository, or code in supplementary materials for the methodology described. |
| Open Datasets | Yes | As our benchmark planning domains, we use 21 classical planning domains from international planning competitions. These domains are airport, blocks, driverlog, freecell, grid, gripper, logistics00, logistics98, miconic, mprime, mystery, pathway, psr-small, pipes-notankage, pipes-tankage, rovers, satellite, storage, tpp, and zenotravel. There are in total 786 problem instances in all 21 domains and we use them to evaluate the planners. |
| Dataset Splits | No | The paper uses '786 problem instances in all 21 domains' from international planning competitions for evaluation. These are individual planning problems, not a single dataset that is split into training, validation, and test sets in the traditional sense. The paper does not specify percentages or counts for such splits within a single dataset, but rather evaluates performance on a collection of distinct problem instances. |
| Hardware Specification | Yes | We ran all experiments reported in this paper on the same high performance computing cluster Gowonda at Griffith University. Each node of the cluster is equipped with Intel Xeon CPU E5-2670 processors @2.60 GHz, FDR 4x Infini Band Interconnect, having system peak performance 18949.2 Gflops. We ran experiments with 4GB memory limit and 60 minute timeout. |
| Software Dependencies | No | As mentioned in Section 3, we use Minion (Gent et al., 2006) as our constraint solver. [...] Our experiments on a number of standard classical planning benchmark domains demonstrate TCPP s efficiency over the original Pa P2 running on SICStus Prolog and our reconstructed and enhanced versions of Pa P2 running on Minion. [...] We use the translator used in Fast Downward (Helmert, 2006) to translate the PDDL description into SAS+ formalism. No specific version numbers are provided for Minion, SICStus Prolog, or Fast Downward. |
| Experiment Setup | Yes | We ran experiments with 4GB memory limit and 60 minute timeout. [...] Minion supports several options for variable ordering, but in our experiments we use the most well-known two: smallest domain first (sdf) and most conflict variable first (conflict). [...] For propagation, Minion supports generalised arc consistency (gac), singleton arc consistency (sac), and a few variants of sac. In our experiments, we use gac and the basic sac as our constraint propagation strategies. [...] In our experiments, we only use the ascending value ordering. [...] Each of TCPP and Pa PR variants running on Minion with the following configurations: 1. sdf-gac: smallest domain first variable ordering and generalised arc consistency; 2. sdf-sac: smallest domain first variable ordering and singleton arc consistency; 3. conf-gac: conflict variable ordering and generalised arc consistency; 4. conf-sac: conflict variable ordering and singleton arc consistency |