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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
The Complexity of LTL on Finite Traces: Hard and Easy Fragments
Authors: Valeria Fionda, Gianluigi Greco
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Motivated by the theoretical results we implemented a reasoner for LTLf, called LTL2SAT1, and compared it with Aalta (Li et al. 2014)... We used the same datasets used in (Li et al. 2014)... Experiments have been executed on a PC Intel Core i5 2,4 GHz, 8GB RAM. For each formula, we measured the ratio between the time required by LTL2SAT to check satisfiability and that required by Aalta. Figure 4 reports the results as percentage stacked bar charts, for each dataset. |
| Researcher Affiliation | Academia | Valeria Fionda and Gianluigi Greco De Ma CS, University of Calabria, Italy EMAIL |
| Pseudocode | No | The paper describes formal frameworks and an encoding approach but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Motivated by the theoretical results we implemented a reasoner for LTLf, called LTL2SAT1... Downloadable at http://ltl2sat.wordpress.com/ |
| Open Datasets | Yes | We used the same datasets used in (Li et al. 2014) |
| Dataset Splits | No | The paper mentions using datasets for comparison but does not provide specific details on training, validation, or test splits such as percentages or sample counts. |
| Hardware Specification | Yes | Experiments have been executed on a PC Intel Core i5 2,4 GHz, 8GB RAM. |
| Software Dependencies | No | LTL2SAT rewrites the input formula into an equivalent Boolean formula and uses glucose (Audemard and Simon 2009) to compute a model. The specific version number for glucose is not provided. |
| Experiment Setup | Yes | When the optimization strategies are not applicable, the size n of the trace to be used in the SAT rewriting is initially set to 1: If no model is found, then n is doubled and the process is repeated until n exceeds the theoretical upper bound (or a 20s time-limit is reached). |