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

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...

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Main Authors: Whitbrook, Amanda, Aickelin, Uwe, Garibaldi, Jonathan M.
Other Authors: Bentley, Peter
Format: Book Section
Published: Springer 2008
Online Access:https://eprints.nottingham.ac.uk/997/
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author Whitbrook, Amanda
Aickelin, Uwe
Garibaldi, Jonathan M.
author2 Bentley, Peter
author_facet Bentley, Peter
Whitbrook, Amanda
Aickelin, Uwe
Garibaldi, Jonathan M.
author_sort Whitbrook, Amanda
building Nottingham Research Data Repository
collection Online Access
description 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.
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institution University of Nottingham Malaysia Campus
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publishDate 2008
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spelling nottingham-9972020-05-04T20:28:01Z https://eprints.nottingham.ac.uk/997/ An idiotypic immune network as a short-term learning architecture for mobile robots Whitbrook, Amanda Aickelin, Uwe Garibaldi, Jonathan M. 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. Springer Bentley, Peter Lee, Doheon Jung, Sungwon 2008 Book Section PeerReviewed 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 http://www.springer.com/computer/foundations/book/978-3-540-85071-7
spellingShingle Whitbrook, Amanda
Aickelin, Uwe
Garibaldi, Jonathan M.
An idiotypic immune network as a short-term learning architecture for mobile robots
title An idiotypic immune network as a short-term learning architecture for mobile robots
title_full An idiotypic immune network as a short-term learning architecture for mobile robots
title_fullStr An idiotypic immune network as a short-term learning architecture for mobile robots
title_full_unstemmed An idiotypic immune network as a short-term learning architecture for mobile robots
title_short An idiotypic immune network as a short-term learning architecture for mobile robots
title_sort idiotypic immune network as a short-term learning architecture for mobile robots
url https://eprints.nottingham.ac.uk/997/
https://eprints.nottingham.ac.uk/997/