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].
Synthesis of Maximally Permissive Strategies for LTLf Specifications
Authors: Shufang Zhu, Giuseppe De Giacomo
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this paper, we show that maximally permissive strategies do exist also for reachability and general LTLf properties, and can in fact be computed with minimal overhead wrt the computation of a single strategy using state-of-the-art tools. |
| Researcher Affiliation | Academia | Shufang Zhu and Giuseppe De Giacomo Sapienza University of Rome, Rome, Italy EMAIL |
| Pseudocode | No | The paper describes the computational steps and formulas, but it does not include a formal pseudocode block or algorithm box. |
| Open Source Code | Yes | Source code at https://github.com/Shufang-Zhu/Syft Max |
| Open Datasets | Yes | We took the LTLf synthesis benchmarks from literature [Tabajara and Vardi, 2019; Bansal et al., 2020; De Giacomo and Favorito, 2021]. |
| Dataset Splits | No | The paper mentions using 'LTLf synthesis benchmarks from literature' but does not specify how these benchmarks were split into training, validation, or testing sets, nor does it describe a cross-validation strategy. |
| Hardware Specification | Yes | All tests were ran on a computer cluster. Each test took exclusive access to a node with Intel(R) Xeon(R) CPU E5-2650 v2 processors running at 2.60GHz. |
| Software Dependencies | Yes | In particular, we based on LTLf synthesis tool LYDIA 2, the overall best performing tool, and implemented our fixpoint-based computation presented in Section 5.1. More specifically, the computation consists of two steps. In the first step, we based on the code of LYDIA, to perform realizability checking, and the nondeterministic non-deferring strategy is also obtained as a side-effect, represented in Binary Decision Diagrams (BDDs). The second step makes use of Boolean operations provided by BDD library CUDD-3.0.0 [Somenzi, 2016] to compute the nondeterministic deferring strategy. |
| Experiment Setup | Yes | Time out was set to 300 seconds. |