The Trembling-Hand Problem for LTLf Planning
Authors: Pian Yu, Shufang Zhu, Giuseppe De Giacomo, Marta Kwiatkowska, Moshe Vardi
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | 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. |