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].
Mimicking Behaviors in Separated Domains
Authors: Giuseppe De Giacomo, Dror Fried, Fabio Patrizi, Shufang Zhu
JAIR 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our objective is to characterize solutions for strategy synthesis for mimicking behaviors under the types of mapping specifications described above, from both the algorithmic and the complexity point of view. We consider several forms of mapping specifications, ranging from simple ones to full ltlf, and for each, we study synthesis algorithms and computational properties. |
| Researcher Affiliation | Academia | Giuseppe De Giacomo EMAIL University of Oxford Dror Fried EMAIL The Open University of Israel Fabio Patrizi EMAIL Sapienza University of Rome Shufang Zhu EMAIL University of Oxford |
| Pseudocode | No | The paper describes methods textually and formally through definitions and theorems, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code, nor does it provide links to any code repositories or supplementary materials containing code for the described methodology. |
| Open Datasets | No | In Section 4.1 we give a detailed example of point-wise mappings from the Pac-Man world. We give a detailed example of target mappings in Section 5.1, from the Rubik s cube world. These are conceptual examples, not empirical datasets. |
| Dataset Splits | No | The paper uses illustrative conceptual examples (Pac-Man, Rubik's Cube) rather than empirical datasets, and therefore does not discuss dataset splits. |
| Hardware Specification | No | The paper primarily focuses on theoretical aspects, including algorithmic and complexity analyses, and does not describe any experimental setup or specific hardware used for running experiments. |
| Software Dependencies | No | The paper discusses theoretical concepts, formalisms, and complexity results, but does not provide details on specific software dependencies or version numbers used for implementation or experimentation. |
| Experiment Setup | No | The paper is theoretical in nature, focusing on algorithmic solutions and complexity, and therefore does not include details on experimental setup, hyperparameters, or training configurations. |