Foundations of distributed artificial intelligence
Foundations of distributed artificial intelligence
Entropy and self-organization in multi-agent systems
Proceedings of the fifth international conference on Autonomous agents
Agent-Based Modeling vs. Equation-Based Modeling: A Case Study and Users' Guide
Proceedings of the First International Workshop on Multi-Agent Systems and Agent-Based Simulation
Individual Level Analysis Using Decision Making Features in Multiagent Based Simulation
Proceedings of the 5th Pacific Rim International Workshop on Multi Agents: Intelligent Agents and Multi-Agent Systems
Towards a Distributed, Environment-Centered Agent Framework
ATAL '99 6th International Workshop on Intelligent Agents VI, Agent Theories, Architectures, and Languages (ATAL),
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
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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.