[Show abstract][Hide abstract] ABSTRACT: In multiagent system research, the work in building simulation model and the effort in developing analysis methods are closely related because building multiagent models relies heavily on new effective analysis methods while justifying new analysis methods needs the simulation results of multiagent models. MASSE (Multiagent Simulation Systematic Explorer) was proposed as an integrated environment which is aimed at (a) to conduct efficient simulation by intelligent scheduling, (b) to conduct intelligent data analysis and knowledge discovery on simulation data, and (c) to develop such analysis methods themselves. Current implementation of MASSE is described in this manuscript, and its usefulness is shown in three aspects: simple accommodation of existing simulators, satisfactory performance for adopting grid computing technique, and systematic data analysis tool. Collaboration among simulation developers and analysts can be strongly supported by using MASSE to incorporate various simulators and unified method for data analysis. We conclude that the current implementation of MASSE is satisfactory.
[Show abstract][Hide abstract] ABSTRACT: In this paper, we explore the learning process of agents by finding and utilizing their mechanical features. Many researches show that agent's learning mechanism always presents phenomena against the world's natural tendency to disorder, which is known as the entropy law. This indicates that there exists energy transmission between agent and its environment. Thus by investigating energy features inside the agent's decision-maker, we successfully visualize and analyze the learning process of agent. Then we explore energy transmission, and give an explanation of the casual relationship between model of agent and behavior of agent-based system, so as to overcome the corresponding long-existing problems in construction and analysis of agent models, as well as agent-based systems. Application to a practical multi-agent system is also demonstrated to support our arguments.