Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Team-Maxmin Equilibrium: Efficiency Bounds and Algorithms
Authors: Nicola Basilico, Andrea Celli, Giuseppe De Nittis, Nicola Gatti
AAAI 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,366 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 7,444 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Furthermore, we study a number of algorithms to ο¬nd and/or approximate an equilibrium, discussing their theoretical guarantees and evaluating their performance by using a standard testbed of game instances. |
| Researcher Affiliation | Academia | Nicola Basilico University of Milan Via Comelico, 39/41 Milano, Italy EMAIL Andrea Celli, Giuseppe De Nittis, Nicola Gatti Politecnico di Milano Piazza Leonardo da Vinci, 32 Milano, Italy EMAIL |
| Pseudocode | Yes | Algorithm 1 Support Enumeration and Algorithm 2 Iterated LP |
| Open Source Code | No | No statement about releasing the source code or a link to a code repository for the methodology described in the paper was found. |
| Open Datasets | Yes | Our experimental setting is based on instances of the Random Games class generated by GAMUT (Nudelman et al. 2004). |
| Dataset Splits | No | No specific dataset split information (e.g., percentages, sample counts, or citations to predefined splits) for training, validation, or testing was found. The paper mentions using '100 game instances for each combination of n and m'. |
| Hardware Specification | Yes | All the algorithms are executed on a UNIX computer with 2.33GHz CPU and 128 GB RAM. |
| Software Dependencies | Yes | Algorithms are implemented in Python 2.7.6, adopting GUROBI 6.5.0 (Gurobi Optimization 2015) for linear mathematical programs, AMPL 20160310 (Fourer, Gay, and Kernighan 1989) and BARON 14.4.0 (Tawarmalani and Sahinidis 2005; Sahinidis 2014) for global optimization programs. |
| Experiment Setup | Yes | We use 100 game instances for each combination of n and m where n P t3, 4, 5u and m is as follows: 5 to 40, step 5, n 3; 50 to 150, step 10, n 3; 5 to 50, step 5, n 4; 5 to 30, step 5, n 5. and We set a timeout of 60 minutes for the resolution of each instance. |