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A combined Short-Term Learning (STL) and Long-Term Learning (LTL) approach tosolving mobile robot navigation problems is presented and tested in both realand simulated environments. The LTL consists of rapid simulations that use aGenetic Algorithm to derive diverse sets of behaviours. These sets are thentransferred to an idiotypic Artificial Immune System (AIS), which forms the STLphase, and the system is said to be seeded. The combined LTL-STL approach iscompared with using STL only, and with using a handdesigned controller. Inaddition, the STL phase is tested when the idiotypic mechanism is turned off.The results provide substantial evidence that the best option is the seededidiotypic system, i.e. the architecture that merges LTL with an idiotypic AISfor the STL. They also show that structurally different environments can beused for the two phases without compromising transferability

An Idiotypic Immune Network as a Short Term Learning Architecture for Mobile Robots