Genetic algorithm seeding of idiotypic networks for mobile-robot navigation
Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into...
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| Format: | Conference or Workshop Item |
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2008
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| Online Access: | https://eprints.nottingham.ac.uk/996/ |
| _version_ | 1848790522776256512 |
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| author | Whitbrook, Amanda Aickelin, Uwe Garibaldi, Jonathan M. |
| author_facet | Whitbrook, Amanda Aickelin, Uwe Garibaldi, Jonathan M. |
| author_sort | Whitbrook, Amanda |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Robot-control designers have begun to exploit the properties of the human immune system in order to
produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one. |
| first_indexed | 2025-11-14T18:13:57Z |
| format | Conference or Workshop Item |
| id | nottingham-996 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T18:13:57Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-9962020-05-04T20:28:01Z https://eprints.nottingham.ac.uk/996/ Genetic algorithm seeding of idiotypic networks for mobile-robot navigation Whitbrook, Amanda Aickelin, Uwe Garibaldi, Jonathan M. Robot-control designers have begun to exploit the properties of the human immune system in order to produce dynamic systems that can adapt to complex, varying, real-world tasks. Jerne’s idiotypic-network theory has proved the most popular artificial-immune-system (AIS) method for incorporation into behaviour-based robotics, since idiotypic selection produces highly adaptive responses. However, previous efforts have mostly focused on evolving the network connections and have often worked with a single, preengineered set of behaviours, limiting variability. This paper describes a method for encoding behaviours as a variable set of attributes, and shows that when the encoding is used with a genetic algorithm (GA), multiple sets of diverse behaviours can develop naturally and rapidly, providing much greater scope for flexible behaviour-selection. The algorithm is tested extensively with a simulated e-puck robot that navigates around a maze by tracking colour. Results show that highly successful behaviour sets can be generated within about 25 minutes, and that much greater diversity can be obtained when multiple autonomous populations are used, rather than a single one. 2008 Conference or Workshop Item PeerReviewed Whitbrook, Amanda, Aickelin, Uwe and Garibaldi, Jonathan M. (2008) Genetic algorithm seeding of idiotypic networks for mobile-robot navigation. In: 5th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2008), 11-15 May 2008, Madiera, Portugal. (Unpublished) Mobile-robot navigation genetic algorithm artificial immune system idiotypic network http://www.icinco.org/icinco2008/cfp.htm |
| spellingShingle | Mobile-robot navigation genetic algorithm artificial immune system idiotypic network Whitbrook, Amanda Aickelin, Uwe Garibaldi, Jonathan M. Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title | Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title_full | Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title_fullStr | Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title_full_unstemmed | Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title_short | Genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| title_sort | genetic algorithm seeding of idiotypic networks for mobile-robot navigation |
| topic | Mobile-robot navigation genetic algorithm artificial immune system idiotypic network |
| url | https://eprints.nottingham.ac.uk/996/ https://eprints.nottingham.ac.uk/996/ |