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].
Parameterised Resource-Bounded ATL
Authors: Natasha Alechina, Stรฉphane Demri, Brian Logan7040-7046
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give a parameter extraction algorithm and prove that the model-checking problem is 2EXPTIMEcomplete. The main contribution of our paper is to show that the maximal value given in (Jurdzinski, Lazi c, and Schmitz 2015) transfers to Par RB ATL, and to show how it can be used for nested temporal goals that include negations and a mixture of reachability and non-termination goals. |
| Researcher Affiliation | Academia | Natasha Alechina Utrecht University Utrecht, The Netherlands EMAIL Stephane Demri LSV, CNRS, ENS Paris-Saclay, University Paris-Saclay Cachan, France EMAIL Brian Logan University of Nottingham Nottingham, UK EMAIL |
| Pseudocode | Yes | Algorithm 1: Computing ฯ0 |
| Open Source Code | No | In future work we plan to implement the algorithm. |
| Open Datasets | No | The paper is theoretical and does not describe the use of any dataset for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not describe the use of any dataset for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe the hardware used for any experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe specific software dependencies with version numbers for implementation or experiments. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |