DNVA: A Tool for Visualizing and Analyzing Multi-agent Learning in Networks

Networks are seen everywhere in our modern life, including the Internet, the Grid, P2P file sharing, and sensor networks. Consequently, researchers in Artificial Intelligence (and Multi-Agent Systems in particular) have been actively seeking methods for optimizing the performance of these networks. A promising yet challenging optimization direction is multi-agent learning: allowing agents to adapt their behavior through interaction with one another. However, understanding the dynamics of an adaptive agent network is complicated due to the large number of system parameters, the concurrency by which the system parameters change, and the delay in the effect/consequence of parameter changes. All these factors make it hard to understand why an adaptive network of agents performed well at some time and poorly at another. In this paper we present a software tool that enables researchers in the multi-agent systems field to visualize and analyze the evolution of adaptive networks. The proposed software customizes and implements techniques from data mining and social network analysis research and augment these techniques in order to analyze local agent behaviors. We use our tool to analyze two domains. In both domains we are able to report and explain interesting observations using our tool.