Analysis of Virtual Agent Communities by Means of AI Techniques and Visualization
The analysis of virtual agent communities combining advanced methods of visualization with traditional AI techniques is presented in this paper. As Multi-Agent Systems (MAS) are becoming more complex and thereby harder to analyze and assess, traditional methods of analysis are becoming insufficient. The main purpose of this research is to develop new methods improving the efficiency of analysis. Extensive assessment on different levels is necessary to analyze MAS and our approach covers from single agent to overall properties of a community. The most substantial merits of this research lie in the utilization of sensitivity analysis together with clustering methods. A simulated artificial life environment with intelligent agents has been used as test bed. We have selected this domain because our long-term goal is to model life as it could be so as to understand life as we know it. However our proposed techniques can be used across multiple domains with intelligent virtual agents.
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