Lightweight Distributed Component-Oriented Multi-Agent Simulation Platform

Abstract

Existing solutions for agent-based systems turn out to be limited in some applications, like agent-based computing or simulations, where very large numbers of clearly defined agents interact heavily within a closed system. In those cases, fully-fledged, FIPA 1 compliant environment introduce unnecessary overhead, but simple tools fail to scale when confronted to bigger problems. In this paper, we introduce an alternative agent environment called AgE, targeted at medium-sized simulation and computational applications, which use multi-agent and computational intelligence paradigms, but does not need full FIPA compliancy, and would benefit from a component-based approach and distributed computing capabilities. After giving a short review of selected popular multi-agent platforms, the main features of AgE are presented. Next, some basic usability topics are addressed. Then the most interesting architectural aspects of the platform are discussed. Finally, AgE possibilities are demonstrated with two example applications.

Agent-Oriented Computing Platform in Python

“…AgE allows to create two types of agents: heavy (implemented as separate threads) and lightweight (executed pseudo-concurrently) ones. However, Java as implementation language is a source of some problems, such as complex mechanism of properties and inability to add properties to already existing object [11].…”

An implementation of the Social Distances Model using multi-GPU systems

“…It is worth emphasizing that there are many ready-to-use simulation platforms that support ABM—see the survey in Allan (2009). Some of these have support for large scale simulations like Repast HPC (Collier and North, 2012) or AgE (Faber et al, 2012; Krzywicki et al, 2013). It is not straightforward to adopt ABM to the GPU-programming paradigm, mainly due to the lack of any assumptions concerning spatial distribution of agents.…”