Logie, Robert; Hall, Jon and Waugh, Kevin
(2008).
| URL: | http://www.informatik.uni-trier.de/~ley/db/conf/ia... |
|---|---|
| Google Scholar: | Look up in Google Scholar |
Abstract
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.
| Item Type: | Book Section |
|---|---|
| Copyright Holders: | 2008 The Authors |
| ISBN: | 972-8924-63-1, 978-972-8924-63-8 |
| Keywords: | agent-systems; stit; multi-agent learning; coaching |
| Academic Unit/School: | Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications Faculty of Science, Technology, Engineering and Mathematics (STEM) |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 19482 |
| Depositing User: | Kevin Waugh |
| Date Deposited: | 28 Apr 2010 10:48 |
| Last Modified: | 24 May 2017 08:13 |
| URI: | http://oro.open.ac.uk/id/eprint/19482 |
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