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..
Strategy Logic with Simple Goals: Tractable Reasoning about Strategies
Authors: Francesco Belardinelli, Wojciech Jamroga, Damian Kurpiewski, Vadim Malvone, Aniello Murano
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimental Evaluation, To answer this question, we have conducted a series of experiments with a scalable benchmark, based on the simple voting and coercion scenario of Examples 1 and 6. |
| Researcher Affiliation | Academia | Francesco Belardinelli1,2 , Wojciech Jamroga3,4 , Damian Kurpiewski3 , Vadim Malvone2 and Aniello Murano5 1 Imperial College London, UK 2 Universit e d Evry, France 3 Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland 4 Interdisciplinary Centre for Security, Reliability, and Trust, Sn T, University of Luxembourg 5 Universit a degli studi di Napoli Federico II , Italy |
| Pseudocode | Yes | Algorithm 1 SL[SG] Model Checking, Algorithm 2 Preimage of a set Y of states |
| Open Source Code | Yes | The tool is available on-line5. 5 https://github.com/slsgijcai19/Strategy Logic Simple Goals. |
| Open Datasets | No | The paper uses models ESVk,n (Extended Simple Voting with k voters and n candidates), constructed as follows. These are generated models, not a publicly available dataset with a specific link, DOI, or citation to an external source. |
| Dataset Splits | No | The paper describes generating models (ESVk,n) for evaluation rather than using a traditional dataset with specified training, validation, and test splits. |
| Hardware Specification | Yes | The experiments were conducted on an Intel Core i7-6700 CPU with dynamic clock speed of 2.60 3.50 GHz and 32 GB RAM, running under 64bit Windows 10. |
| Software Dependencies | No | The paper mentions 'Python 3' but does not specify a precise version (e.g., Python 3.x) or list any other software dependencies with version numbers. |
| Experiment Setup | Yes | The timeout was set to 5 hours. The experimental results are presented in Tab. 1. All times are given in seconds. ... In the experiments, we have only used models with n = 2. Thus, the number of voters (k) was the sole scaling factor. |