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Title:
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TOWARDS MINING FOR INFLUENCE IN A MULTI AGENT ENVIRONMENT |
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Author(s):
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Robert Logie , Jon G. Hall , Kevin G. Waugh |
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ISBN:
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978-972-8924-63-8 |
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Editors:
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Hans Weghorn and Ajith P. Abraham |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Agent-systems, stit, multi-agent learning, coaching. |
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Type:
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Short Paper |
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First Page:
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97 |
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Last Page:
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101 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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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. |
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