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].
Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search
Authors: Redha Taguelmimt, Samir Aknine, Djamila Boukredera, Narayan Changder, Tuomas Sandholm
IJCAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments over several common value distributions, we show that the hybridization of these techniques in SMART is faster than the fastest prior algorithms (ODP-IP, BOSS) in generating optimal solutions across all the value distributions. |
| Researcher Affiliation | Collaboration | Redha Taguelmimt1 , Samir Aknine1 , Djamila Boukredera2 , Narayan Changder3 and Tuomas Sandholm4,5,6,7 1Univ Lyon, UCBL, CNRS, INSA Lyon, Centrale Lyon, Univ Lyon 2, LIRIS, UMR5205, Lyon, France 2Laboratory of Applied Mathematics, Faculty of Exact Sciences, University of Bejaia, Bejaia, Algeria 3TCG Centres for Research and Education in Science and Technology, Kolkata, India 4Computer Science Department, Carnegie Mellon University, Pittsburgh, USA 5Strategy Robot, Inc. 6Strategic Machine, Inc. 7Optimized Markets, Inc. |
| Pseudocode | Yes | Algorithm 1: Size Sets Definition (SSD) Algorithm |
| Open Source Code | No | The paper states 'We implemented SMART in Java' but does not provide any explicit statement about open-sourcing the code or a link to a repository for their implementation. |
| Open Datasets | Yes | We compared the algorithms using the following value distributions: Modified Normal [Rahwan et al., 2012], Beta, Exponential, Gamma [Michalak et al., 2016], Normal [Rahwan et al., 2007], Uniform [Larson and Sandholm, 2000], Modified Uniform [Service and Adams, 2010], Zipf, SVA Beta and Weibull [Changder et al., 2020]. |
| Dataset Splits | No | The paper mentions running each algorithm '50 times' for each distribution and number of agents, which refers to repetitions, but does not provide specific train/validation/test splits for the data or problem instances. |
| Hardware Specification | Yes | The algorithms were run on an Intel Xeon 2.30GHz E5-2650 CPU with 256GB of RAM. |
| Software Dependencies | No | The paper states 'We implemented SMART in Java' but does not specify a Java version or other software dependencies with version numbers. |
| Experiment Setup | Yes | For GRAD, we considered values of ω {10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, 100%}. For each distribution and number of agents, we ran each algorithm 50 times. |