Towards mining for influence in a multi agent environment

Logie, Robert; Hall, Jon and Waugh, Kevin (2008). Towards mining for influence in a multi agent environment. In: Weghorn, Hans and Abraham, Ajith eds. 2nd European Conference on Data Mining (ECDM 2008). Lisbon, Portugal: IADIS press, pp. 97–101.



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 large amounts of 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.

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