Two-timescale learning using idiotypic behaviour mediation for a navigating mobile robot

Abstract

A combined short-term learning (STL) and long-term learning (LTL) approach to solving mobile-robotnavigation problems is presented and tested in both the real and virtual domains. The LTL phase consistsof rapid simulations that use a genetic algorithm to derive diverse sets of behaviours, encoded as variablesets of attributes, and the STL phase is an idiotypic artificial immune system. Results from the LTL phaseshow that sets of behaviours develop very rapidly, and significantly greater diversity is obtained whenmultiple autonomous populations are used, rather than a single one. The architecture is assessed undervarious scenarios, including removal of the LTL phase and switching off the idiotypic mechanism in theSTL phase. The comparisons provide substantial evidence that the best option is the inclusion of both theLTL phase and the idiotypic system. In addition, this paper shows that structurally different environmentscan be used for the two phases without compromising transferability