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].
Pushdown Multi-Agent System Verification
Authors: Aniello Murano, Giuseppe Perelli
IJCAI 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we investigate the model-checking problem of pushdown multi-agent systems for ATL specifications. To this aim, we introduce pushdown game structures over which ATL formulas are interpreted. We show an algorithm that solves the addressed model-checking problem in 3EXPTIME. We also provide a 2EXPSPACE lower bound by showing a reduction from the word acceptance problem for deterministic Turing machines with doubly exponential space. |
| Researcher Affiliation | Academia | Aniello Murano1 and Giuseppe Perelli1,2 1Universit a degli studi di Napoli Federico II , 2University of Oxford |
| Pseudocode | No | The paper describes algorithmic concepts but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code. |
| Open Datasets | No | This is a theoretical paper and does not involve training models on publicly available datasets. |
| Dataset Splits | No | This is a theoretical paper and does not discuss training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper and does not discuss hardware specifications for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe experimental setup details like hyperparameters or training configurations. |