Towards Mining for Influence in a Multi Agent Environment

Multi agent learning systems pose an interesting set of problems: in large environments agents may develop localised behaviour patterns that are not necessarily optimal; in a pure agent system there is no globally aware element which can identify and eliminate retrograde behaviour; and as systems scale they may produce large amounts of data, a system may have in the order of 106 cells with 105 agents, each generating data. This position paper introduces research that combines data mining with a logical framework to allow agents in large systems to learn about their environment and develop behaviours appropriate to satisfying system norms. We build from traditional multi agent systems, adding a novel process algebraic approach to co-operation using data mining techniques to identify co-operative behaviours worth learning. The result is predicted to be a learning system in which agents form collectives increasing their `mutual influence' on the environment. KEYWORDS Agent-systems, ...

• Type: Conference
• Year: 2008
• Where: IADIS
• Authors: Robert Logie, Jon G. Hall, Kevin G. Waugh
• Keywords: Agent Learning Systems, IADIS 2008, Internet Technology, Large Environments Agents, Multi Agent