ALIFE Proceedings of the sixth international conference on Artificial life
Active vision and feature selection in evolutionary behavioral systems
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Decentralized control system for autonomous navigation based on an evolved artificial immune network
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
The balance between initial training and lifelong adaptation in evolving robot controllers
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Idiotypic Immune Networks in Mobile-Robot Control
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organisation for survival in complex computer architectures
SOAR'09 Proceedings of the First international conference on Self-organizing architectures
EA'09 Proceedings of the 9th international conference on Artificial evolution
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
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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 hand-designed 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.