Extensive-Form Perfect Equilibrium Computation in Two-Player Games

Authors: Gabriele Farina, Nicola Gatti

AAAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Theoretical We study the problem of computing an Extensive-Form Perfect Equilibrium (EFPE) in 2-player games. This equilibrium concept refines the Nash equilibrium requiring resilience with respect to a specific vanishing perturbation, representing mistakes of the players at each decision node. The scientific challenge is intrinsic to the EFPE definition: it requires a perturbation over the agent form, but the agent form is computationally inefficient due to the presence of highly nonlinear constraints. We show that the sequence form can be exploited in a non-trivial way and that, for generalsum games, finding an EFPE is equivalent to solving a suitably perturbed linear complementarity problem. We prove that Lemke s algorithm can be applied, showing that computing an EFPE is PPAD-complete. In the notable case of zero-sum games, the problem is in FP and can be solved by linear programming.
Researcher Affiliation Academia Gabriele Farina Computer Science Department Carnegie Mellon University 5000 Forbes Avenue Pittsburgh, PA 1513, USA gfarina@cs.cmu.edu Nicola Gatti Dipartimento di Elettronica, Informazione e Bioingegneria Politecnico di Milano Piazza Leonardo da Vinci, 32 I-20133, Milan, Italy nicola.gatti@polimi.it
Pseudocode Yes Algorithm 1 procedure FIND-EFPE
Open Source Code No The paper does not provide an explicit statement or link for open-source code for the described methodology. The arXiv link provided is for the paper itself, not source code.
Open Datasets No The paper is theoretical and does not involve the use of datasets for training or evaluation.
Dataset Splits No The paper is theoretical and does not involve data splitting for training, validation, or testing.
Hardware Specification No The paper is theoretical and does not describe specific hardware used for experiments.
Software Dependencies No The paper mentions 'Lemke s algorithm' and 'linear programming' but does not specify particular software or library names with version numbers.
Experiment Setup No The paper is theoretical and does not describe an experimental setup with specific hyperparameters or training configurations.