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..
Counterfactual Regret Minimization in Sequential Security Games
Authors: Viliam Lisy, Trevor Davis, Michael Bowling
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our approach on two security-inspired domains. |
| Researcher Affiliation | Academia | Department of Computing Science University of Alberta, Edmonton, AB, Canada T6G 2E8 EMAIL |
| Pseudocode | Yes | The pseudocode is presented in Figure 2. |
| Open Source Code | No | No explicit statement about providing access to the paper's own open-source code was found. |
| Open Datasets | Yes | Transit game (TG) is the game used for evaluation in (Bosansky et al. 2015). Ticket inspection game (IG) is based on (Jiang et al. 2013). |
| Dataset Splits | No | No explicit mention of training/test/validation dataset splits or cross-validation was found for the game environments described. |
| Hardware Specification | No | The paper mentions using 'the computing resources of Compute Canada and Calcul Quebec,' but no specific hardware details like GPU/CPU models are provided. |
| Software Dependencies | Yes | For solving LPs, we used IBM CPLEX 12.51. |
| Experiment Setup | Yes | The precision of CPLEX is by default set to 10-6. |