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].
Temporal Planning with Clock-Based SMT Encodings
Authors: Jussi Rintanen
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare our result to the Shin-Davis style step scheme and ITSAT which is one of the the strongest temporal planners [Rankooh and Ghassem-Sani, 2015], shown to outperform earlier planners [Gerevini et al., 2006; Coles et al., 2010; Eyerich et al., 2012; Lu et al., 2013]. Experimentation was based on Rintanenās [2015a] code, including the discretization method to eliminate time variables whenever possible. We used Math SAT 5.3.6 [Audemard et al., 2005; Cimatti et al., 2013] for instances with real-valued variables, and Preco SAT [Biere, 2010] for purely Boolean ones. Experiments were run in Intel Xeon CPUs. From the SMT approach, SD is the baseline encoding [Rintanen, 2015a]. C is obtained from SD by using the clockbased encodings of resource constraints and delays (Sections 5 and 6). R additionally uses summarized steps (Section 4). Table 1 lists the number of IPC instances solved in 1800 seconds. ITSAT doesnāt handle numeric variables and cannot solve the problems indicated with a dash. Differences between SD, C and R are not clearly visible here: many problem series are solved (almost) completely by all planners, some series are too difļ¬cult, and e.g. Parking gets fully discretized and all three planners use the same SAT encoding. Figure 3 plots makespans for instances solved by both ITSAT and R, with ITSAT makespans often close to twice those of R. R makespans are higher only with few instances of TMS. On these, C makespans are the same as ITSATās. Figure 4 shows |
| Researcher Affiliation | Academia | Jussi Rintanen Aalto University, Department of Computer Science, Helsinki, Finland Also afļ¬liated with Grifļ¬th University, Brisbane, Australia, and the Helsinki Institute for Information Technology, Finland. |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions āIPC instancesā and āstandard benchmark problemsā but does not provide a specific link, DOI, repository, or formal citation (with authors and year) for accessing these datasets. |
| Dataset Splits | No | The paper does not explicitly specify exact percentages or counts for training, validation, and test dataset splits. |
| Hardware Specification | Yes | Experiments were run in Intel Xeon CPUs. |
| Software Dependencies | Yes | We used Math SAT 5.3.6 [Audemard et al., 2005; Cimatti et al., 2013] for instances with real-valued variables, and Preco SAT [Biere, 2010] for purely Boolean ones. |
| Experiment Setup | No | The paper does not provide specific details on hyperparameters (e.g., learning rate, batch size) or detailed system-level training settings. |