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..
Efficient Computation of Approximate Equilibria in Discrete Colonel Blotto Games
Authors: Dong Quan Vu, Patrick Loiseau, Alonso Silva
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform numerical experiments that show that the proposed strategy provides a fast and good approximation to the equilibrium even for moderate numbers of battlefields. Our experiments run on a computer with an Intel core i5-7500U 2.60GHz processor and 8GB of RAM. |
| Researcher Affiliation | Collaboration | 1 Nokia Bell Labs, Nokia Paris-Saclay, 91620 Nozay, France 2 Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France 3 Max Planck Institute for Software Systems (MPI-SWS), Saarbr ucken, Germany |
| Pseudocode | Yes | Algorithm 1: DIU strategy generation algorithm. Algorithm 2: Dynamic programing algorithm searching for player B s best-response (tie-breaking rule α = 0). |
| Open Source Code | Yes | Our code for these experiments can be found at https://github. com/dongquan11/Approx discrete Blotto |
| Open Datasets | No | For each set of values n, m, p, we independently generate a value for each battlefield uniformly distributed in [vmin, vmax], with vmin = 1 and vmax = 8. The paper describes synthetic data generation but does not provide access information for a public dataset. |
| Dataset Splits | No | The paper describes generating game instances and running simulations, but it does not specify train/validation/test splits for a dataset. |
| Hardware Specification | Yes | Our experiments run on a computer with an Intel core i5-7500U 2.60GHz processor and 8GB of RAM. |
| Software Dependencies | No | We construct several numerical experiments using R. The paper mentions the software 'R' but does not specify a version number or any other software dependencies with versions. |
| Experiment Setup | Yes | In all the experiments, we keep α = 0 and λ = p/m fixed, thus the values of m and p always have the same growth rate (up to the multiplicative constant λ); and we vary n and m. For each set of values n, m, p, we independently generate a value for each battlefield uniformly distributed in [vmin, vmax], with vmin = 1 and vmax = 8. Then, for each instance of n, m, p, (v1, v2, . . . , vn) we run the simulation 3 times, each time computing the e CDFs with K = 10 n to ensure not to affect the evaluation on ε and running Algorithm 2 with these e CDFs to compute ε, and take the average of results. |