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..
A Decision Procedure for a Fragment of Linear Time Mu-Calculus
Authors: Yao Liu, Zhenhua Duan, Cong Tian
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | we propose a simple and intuitive GPG-based decision procedure for checking satisfiability of Gµ formulas which has the same time complexity as the decision problem of Linear Temporal Logic (LTL). Theorem 2 Every closed formula ' can be transformed into GPF. Theorem 3 Transforming a formula φ into GPF by algorithm GPFTr can be completed in 2O(|φ|). Theorem 6 A closed formula φ is satisfiable iff a -path can be found in Gφ. Theorem 8 For a given closed formula φ, the GPG-based decision procedure can be done in 2O(|φ|). |
| Researcher Affiliation | Academia | ICTT and ISN Laboratory, Xidian University |
| Pseudocode | Yes | Algorithm 1 GPFTr(φ), Algorithm 2 GPGCON(φ), Algorithm 3 Nu Search(n0) |
| Open Source Code | No | The paper does not contain any statement about releasing open-source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not involve datasets or empirical training. Therefore, it does not mention public or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve datasets or empirical validation. Therefore, it does not provide dataset splits for validation. |
| Hardware Specification | No | This is a theoretical paper and does not describe any experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper and does not describe any experiments that would require specific software dependencies with version numbers. No such details are provided. |
| Experiment Setup | No | This is a theoretical paper and does not involve an experimental setup with hyperparameters or training settings. No such details are provided. |