A platform for large-scale game-playing multi-agent systems on a high performance computing infrastructure

Abstract

The simulation of societies requires vast amounts of computing resources, which must be managed over distributed or high performance computing infrastructures to provide for cost-effective experimentation. To that end, this paper presents a novel platform for the segmentation and management of social simulation experiments in game-playing multi-agent systems; the platform, also serves as a working proof of concept for similar experiments. The platform is managed through a web-based graphical user interface, to combine the advantages of powerful grid infrastructure middleware and sophisticated workflow systems in a way that some generic functionality is sacrificed for the benefit of obtaining a smooth and brief learning curve, without compromising security. The paper sets out the architecture and implementation details of the platform and demonstrates its use with two sample games, RLGame and Rock Scissors Paper, to underline the scale of the experiments and to indicate the class of social simulation problems that it can help investigate. The platform can be loosely coupled with analytics software for data mining; for our sample problems, this analysis leads to associating the learning mechanism each agent employs with its eventual performance ranking.