Iravani, Pejman; Johnson, Jeff H. and Rapanotti, Lucia
(2005).
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Abstract
A new method is presented for the analysis of complex multi-agent robotic behaviours, based on observing behaviour and abstracting models on which to base control. This involves (i) building an appropriate representation of the scene using the `best' features, (ii) classifying each scene by its features, and (iii) learning the correlation between scene classes and the actions performed in each. A novel aspect is the use of Q-analysis to investigate relational structure between many possible observable features, and congurations in the scene. Q-analysis helps in the selection of appropriate features and is the basis of our combinatorial classification. The methods and algorithms presented are experimentally validated using data from the RoboCup simulation league. We conclude that Q-analysis could be a powerful new approach in robot control.
| Item Type: | Conference Item |
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| Academic Unit/Department: | Mathematics, Computing and Technology > Design, Development, Environment and Materials Mathematics, Computing and Technology > Computing |
| Interdisciplinary Research Centre: | Centre for Research in Computing (CRC) |
| Item ID: | 19138 |
| Depositing User: | Lucia Rapanotti |
| Date Deposited: | 08 Dec 2009 11:44 |
| Last Modified: | 02 Dec 2010 20:42 |
| URI: | http://oro.open.ac.uk/id/eprint/19138 |
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