Towards a Goal-oriented Agent-based Simulation framework for High-Performance Computing

TL;DR

This paper introduces a novel framework integrating goal-oriented agents into large-scale high-performance computing simulations, enabling complex agent reasoning at scale.

Contribution

It presents a new model for goal-oriented agents in HPC and demonstrates its implementation using PyCOMPSs for scalable, complex agent-based simulations.

Findings

Successful integration of goal-oriented agents into large HPC simulations

Enhanced reasoning capabilities in micro-simulations with many agents

Potential for scalable, complex cognitive agent simulations in HPC environments

Abstract

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.