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..
Possibilistic Games with Incomplete Information
Authors: Nahla Ben Amor, Helene Fargier, Régis Sabbadin, Meriem Trabelsi
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on variants of the GAMUT problems confirm the feasibility of this approach. |
| Researcher Affiliation | Academia | 1ISG-Tunis, Universit e de Tunis 2IRIT-CNRS, Universit e de Toulouse 3INRA-MIAT, Universit e de Toulouse |
| Pseudocode | No | The paper presents mathematical constraints for the MILP formulation but does not include structured pseudocode or an algorithm block. |
| Open Source Code | Yes | The implementation of the T G and MILP solver are available online [Ben Amor et al., 2019]. The possibilistic games page. https://www.irit.fr/ Helene.Fargier/ Possibilistic Games.html, 2019. |
| Open Datasets | Yes | To conduct our experimental study, we developed a novel Π-game generator based on GAMUT [Nudelman et al., 2004], a suite of classical normal form games (with complete information) generators (following the approach of [Ceppi et al., 2009] for the generation of Bayesian games). |
| Dataset Splits | No | The paper describes generating instances for testing the MILP solver and comparing it to another method, but it does not specify explicit training, validation, and test splits for a machine learning model, as the problem is about solving for equilibria rather than training a predictive model. |
| Hardware Specification | Yes | All experiments were conducted on an Intel Xeon E5540 processor and 64GB RAM workstation. |
| Software Dependencies | Yes | We used CPLEX [CPLEX, 2009] as a MILP solver. ... IBM ILOG CPLEX. V12. 1: User s manual for CPLEX, 2009. ... We also implemented the transformation of the T G and MILP solver are available online [Ben Amor et al., 2019] in Java 8. |
| Experiment Setup | Yes | More precisely, to generate a Π-game version of a GAMUT problem (e.g., the Covariant game), we need as inputs: the number of degrees in , the number n of players, the class of game and if necessary the number of actions |Ai| and of types |Θi| for each player i. ... In our evaluation, we bounded the execution time to 10 minutes as in [Sandholm et al., 2005; Porter et al., 2008] experiments. |