Graph-based methods for the analysis of large-scale multiagent systems

Multiagent systems are often characterized by complex, and sometimes unpredictable interactions amongst their autonomous components. While these systems can provide robust and scalable solutions to a variety of problems, the inherent complexity presents a barrier to their analysis, understanding, debugging and modification. In the work presented here, we seek to overcome this problem by modeling the execution of a multiagent system as a graph, which admits the application of techniques from the wellestablished field of algorithmic graph theory. In particular, we employ graph search and isomorphism computation to find repeating patterns of communication within a multiagent simulation. We argue, and demonstrate empirically, that the graph, even if it is too large to fit into main memory, carries useful properties, which allow these operations to be performed efficiently. We further show that the resulting patterns (which tend to be manageable in size) present a useful view of a simulati...

• Type: Conference
• Year: 2009
• Where: ATAL
• Authors: Wilbur Peng, William Krueger, Alexander Grushin, Patrick Carlos, Vikram Manikonda, Michel Santos
• Keywords: Algorithmic Graph Theory, Artificial Intelligence, ATAL 2009, Multiagent, Multiagent Systems