MACE3J

Abstract

Scientific study of multi-agent systems (MAS) requires infrastructure such as development testbeds and simulation tools for repeatable, controlled experiments with MAS structure and behavior. Testbeds and simulation tools are also critical for MAS education and development. A number of MAS testbeds currently exist, but to date none meets in a comprehensive way criteria laid out by many analysts for general, scientific, experimental study of MAS by a large community. Moreover, none really scales to very large MAS or exploits the power of modern distributed computing environments such as large multiprocessor clusters and computational grids. Because of this, and specifically to fulfill widespread need for tools supporting distributed collaborative scientific research in large-scale, large-grain MAS, we created the MACE3J system, a successor to the pioneering MACE testbed.MACE3J is a Java-based MAS simulation, integration, and development testbed, with a supporting library of components, examples, and documentation, distributed freely. MACE3J currently runs on single- and multiprocessor workstations, and in large multiprocessor cluster environments. The MACE3J design is multi-grain, but gives special attention to simulating very large communities of large-grain agents. It exhibits a significant degree of scalability, and has been effectively used in fast simulations of over 5,000 agents, 10,000 tasks, and 10M messages, and on multiprocessor configurations of up to 48 processors, with a future target of at least 1000 processors.This paper presents MACE3J design criteria and our approach to a number of critical tradeoffs that, to our knowledge, have not previously been treated explicitly in MAS literature or platforms. We present the innovative features of the MACE3J architecture that contribute to its breadth, flexibility and scalability, and finally give results from the use of MACE3J in real experiments in realistic MAS domains, both simple and complex.