Currently, agent-based simulation frameworks force the user to choose between simulations involving a large number of agents (at the expense of limited agent reasoning capability) or simulations including agents with increased reasoning capabilities (at the expense of a limited number of agents per simulation). This paper describes a first attempt at putting goal-oriented agents into large agent-based (micro-)simulations. We discuss a model for goal-oriented agents in High-Performance Computing (HPC) and then briefly discuss its implementation in PyCOMPSs (a library that eases the parallelisation of tasks) to build such a platform that benefits from a large number of agents with the capacity to execute complex cognitive agents.
@article{arxiv.1911.10055,
title = {Towards a Goal-oriented Agent-based Simulation framework for High-Performance Computing},
author = {Dmitry Gnatyshak and Luis Oliva-Felipe and Sergio Álvarez-Napagao and Julian Padget and Javier Vázquez-Salceda and Dario Garcia-Gasulla and Ulises Cortés},
journal= {arXiv preprint arXiv:1911.10055},
year = {2019}
}