Abstract
Previous work has shown that robot navigation systems that employ an architecture based upon the idiotypic network theory of the immune system have an advantage over control techniques that rely on reinforcement learning only. This is thought to be a result of intelligent behaviour selection on the part of the idiotypic robot. In this paper an attempt is made to imitate idiotypic dynamics by creating controllers that use reinforcement with a number of different probabilistic schemesto select robot behaviour. The aims are to show that the idiotypic system is not merely performing some kind of periodic random behaviour selection, and to try to gain further insight into the processes that govern the idiotypic mechanism. Trialsare carried out using simulated Pioneer robots that undertake navigation exercises. Results show that a scheme that boosts the probability of selecting highly-ranked alternative behaviours to 50% during stall conditions comes closest to achieving the properties of the idiotypic system, but remains unable to match it in terms of all round performance.
Highlights
An artificial immune system (AIS) is a computational algorithm that attempts to mimic properties of the vertebrate immune system in order to solve complex problems
ID performs better even when a probabilistic system uses some form of inherent intelligence, for example basing the likelihood of antibody selection upon the current reinforcement learning (RL) scores or boosting the probability of selecting an alternative to α under certain conditions (R6 – R9)
This suggests that the idiotypic dynamics are doing more than merely raising the rate of antibody change when the robot is in difficulty
Summary
An artificial immune system (AIS) is a computational algorithm that attempts to mimic properties of the vertebrate immune system in order to solve complex problems. There are a number of different types including clonal selection-based algorithms [4], negative selection-based algorithms [5] and idiotypic networks [6] The latter group is inspired by Jerne’s network theory of the immune system [1], which asserts that suppression and stimulation between antibodies plays an important role in the immune response. Within the domain of mobile robotics, the idiotypic network has remained a popular choice of AIS, with most researchers opting to implement Farmer et al.’s computational model [2] of Jerne’s theory This is largely because the network of stimulation and suppression between antibodies (analogous to behaviours in these systems) is thought to provide a means of achieving a decentralized behaviour-selection mechanism for the robot. He suggests that antibody concentration levels are influenced by inter-antibody activity, i.e. antibodies that are recognized by others are suppressed and reduce in concentration and those that do the recognizing are stimulated and increase in concentration.
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