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..
Learning Coalition Structures with Games
Authors: Yixuan Even Xu, Chun Kai Ling, Fei Fang
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments to evaluate IG in the auction setting and the results align with our theoretical analysis. We conduct experiments to evaluate the performance of our algorithms in practice. |
| Researcher Affiliation | Academia | 1Tsinghua University 2Columbia University 3Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1: Iterative Grouping (IG) and Algorithm 2: IG with Auctions (Auction IG) |
| Open Source Code | Yes | The source codes can be found at https://github.com/Yixuan Even Xu/coalition-learning. |
| Open Datasets | No | We model this more realistic setting by assuming that the values are drawn from an item pool V, which is a distribution U[0, 1]n over Rn. For each setting, we fix n and either fix m or sample m from U[n]. Then, we synthesize a coalition structure S with exactly n agents and m coalitions at random. The paper describes a synthetic data generation process rather than using an existing publicly available dataset with concrete access information. |
| Dataset Splits | No | For each setting, we fix n and either fix m or sample m from U[n]. Then, we synthesize a coalition structure S with exactly n agents and m coalitions at random. We then run Auction IG, check the correctness of its output, and record the sample complexity (the total number of samples used). The paper describes a simulation-based evaluation without explicit train/validation/test splits commonly found in supervised learning. |
| Hardware Specification | Yes | We implement it in Python and evaluate it on a server with 56 cores and 504G RAM, running Ubuntu 20.04.6. |
| Software Dependencies | No | We implement it in Python and evaluate it on a server with 56 cores and 504G RAM, running Ubuntu 20.04.6. No specific library versions are provided. |
| Experiment Setup | Yes | Experiment setup. We evaluate Auction IG under different settings of n and m, where n is the number of agents and m is the number of coalitions. For each setting, we fix n and either fix m or sample m from U[n]. Then, we synthesize a coalition structure S with exactly n agents and m coalitions at random. We then run Auction IG, check the correctness of its output, and record the sample complexity (the total number of samples used). |