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 Trembling-Hand Problem for LTLf Planning
Authors: Pian Yu, Shufang Zhu, Giuseppe De Giacomo, Marta Kwiatkowska, Moshe Vardi
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We formally show the correctness of our solution techniques and demonstrate their effectiveness experimentally through a proofof-concept implementation. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Oxford, UK 2 Department of Computer Science, Rice University, USA |
| Pseudocode | Yes | Algorithm 1 State Pruning |
| Open Source Code | Yes | The implementation details of our algorithms and experiments can be found on Git Hub: https://github.com/piany/Tremblinghand_LTLf. |
| Open Datasets | No | The paper describes a human-robot co-assembly problem used as a case study, but it does not specify a publicly available dataset with concrete access information (link, DOI, or formal citation) for training or evaluation. |
| Dataset Splits | No | The paper does not provide specific details on training, validation, and test dataset splits as it relies on a simulated environment rather than a standard empirical dataset. |
| Hardware Specification | Yes | All experiments were carried out on a Macbook Pro (2.6 GHz 6-Core Intel Core i7 and 16 GB of RAM). |
| Software Dependencies | No | The paper states "We implemented the solution technique described in Sec. 4, which subsumes the method described in Sect. 3, in Python, and use LYDIA [De Giacomo and Favorito, 2021] for LTLf-to-DFA construction." While Python and LYDIA are mentioned, specific version numbers for Python or any other libraries are not provided. |
| Experiment Setup | Yes | In our experiments, the convergence precision for the value iteration in Eqn. (2) was set to 10-3. |