Dynamic analysis of agents' behaviour: Combining alife, visualization and AI

The analysis of an agent or agent communities combining advanced methods of visualization with traditional AI techniques is presented in this paper. However this approach can be used for arbitrary Multi-Agent System (MAS), it was primarily developed to analyse systems falling into Artificial Life domain. Traditional methods are becoming insufficient as MAS are becoming more complex and therefore novel approaches are needed. Our approach builds upon various techniques to deliver means for assessment on multiple levels ranging from single agent to overall properties of an agent community. Our visualization tools suite utilizes novel visualization methods together with traditional AI techniques such as sensitivity analysis and clustering. Among others it offers visualization of many agent's parameters along time, correspondence between current/previous states (of an agent community), resulting behaviour, grouping of agents based on dominant properties etc. This transparent approach emphasizes MAS dynamics through automatic discovery of its tendency. Agent position inside virtual environment together with overview over the whole time interval adds strong contextual information to analysis. Position in our understanding is not limited to geometrical meaning, but covers also the space of dynamically changing constraints for action selection. A simulated artificial life environment with intelligent agents has been used as a test bed. We have selected this particular domain because our long-term goal is to model life as it could be so as to understand life, as we know it.