Possibilistic Games with Incomplete Information

Authors: Nahla Ben Amor, Helene Fargier, Régis Sabbadin, Meriem Trabelsi

IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | 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.