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].
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. |