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..
Robust Solutions for Multi-Defender Stackelberg Security Games
Authors: Dolev Mutzari, Yonatan Aumann, Sarit Kraus
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we introduce a robust model for MSSGs, which admits solutions that are resistant to small perturbations or uncertainties in the game s parameters. First, we formally define the notion of robustness, as well as the robust MSSG model. Then, for the non-cooperative setting, we prove the existence of a robust approximate equilibrium in any such game, and provide an efficient construction thereof. For the cooperative setting, we show that any such game admits a robust approximate α-core, provide an efficient construction thereof, and prove that stronger types of the core may be empty. |
| Researcher Affiliation | Academia | Dolev Mutzari , Yonatan Aumann , Sarit Kraus Department of Computer Science, Bar Ilan University, Ramat Gan, Israel EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 ALLOC Input: value c, c [0, 1], G = (N, T , K, P, R) Output: a strategy profile X = (xi,t) |
| Open Source Code | No | The paper mentions 'Complete proofs can be found at [Mutzari et al., 2022]', but does not state that the code for the methodology is open-source or provide a link. |
| Open Datasets | No | The paper is theoretical and does not describe using datasets for training or evaluation. Therefore, no information about public datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not describe using datasets for training or evaluation. Therefore, no information about dataset splits is provided. |
| 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 describe any specific software dependencies or versions for experimental replication. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |