An idiotypic immune network as a short-term learning architecture for mobile robots

Whitbrook, Amanda, Aickelin, Uwe and Garibaldi, Jonathan M. (2008) An idiotypic immune network as a short-term learning architecture for mobile robots. In: Artificial immune systems: 7th international conference, ICARIS 2008, Phuket, Thailand, August 10-13, 2008: proceedings. Lecture notes in computer science (5132). Springer, Berlin, pp. 266-278. ISBN 9783540850717

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A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach to solving mobile robot navigation problems is presented and tested in both real and simulated environments. The LTL consists of rapid simulations

that use a Genetic Algorithm to derive diverse sets of behaviours. These sets are then transferred to an idiotypic Artificial Immune System (AIS), which forms the STL phase, and the system is said to be seeded. The combined

LTL-STL approach is compared with using STL only, and with using a handdesigned controller. In addition, the STL phase is tested when the idiotypic mechanism is turned off. The results provide substantial evidence that the best option is the seeded idiotypic system, i.e. the architecture that merges LTL with an idiotypic AIS for the STL. They also show that structurally different environments can be used for the two phases without compromising transferability.

Item Type: Book Section
Schools/Departments: University of Nottingham, UK > Faculty of Science > School of Computer Science
Depositing User: Aickelin, Professor Uwe
Date Deposited: 30 Jan 2009 13:49
Last Modified: 04 May 2020 20:28

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