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..
Blameworthiness in Security Games
Authors: Pavel Naumov, Jia Tao2934-2941
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper defines blameworthiness of the defender and the attacker in security games using the principle of alternative possibilities and provides a sound and complete logical system for reasoning about blameworthiness in such games. |
| Researcher Affiliation | Academia | Pavel Naumov,1 Jia Tao2 1Tulane University, 2Lafayette College EMAIL, EMAIL |
| Pseudocode | No | The paper defines logical systems and rules but does not present any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is theoretical and does not mention any open-source code for its methodology. |
| Open Datasets | No | The paper uses illustrative game examples (G1, G2, G3) but does not perform experiments on, or provide access information for, any public or open datasets. |
| Dataset Splits | No | The paper is theoretical and does not include any experimental validation or dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not provide details about an experimental setup, hyperparameters, or training configurations. |