Learning Coalition Structures with Games
Authors: Yixuan Even Xu, Chun Kai Ling, Fei Fang
AAAI 2024 | Conference PDF | Archive PDF | Plain Text | 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). |