An Idiotypic Network Approach to Task Allocation in the Multi-robot Domain - Use of an Artificial Immune System to Moderate the Greedy Solution

This paper presents and explains a set of equations for governing simultaneous task allocation in multi-robot systems and describes how they are used to construct a novel algorithm the Idiotypic Task Allocation Algorithm (ITAA); the equations are based on Farmer's model of an idiotypic immune network but are adapted to include 2-dimensional stimulation and suppression and the use of affinity rather than concentration levels to select antibodies. This novel approach is taken to render the model suitable for simultaneous task allocation where robots must act individually; other idiotypic algorithms have only been applicable to problems where many robots are required to perform one task at a time using swarming behaviours. The paper describes the analogy between idiotypic network theory and the problem of task allocation and shows how the former can be used to increase the fitness of solutions to the latter, also discussing the types of Multi-Robot Task Allocation (MRTA) problem that might benefit from this approach. The results of applying ITTA to a number of simulated mine-clearance problems (with increasing numbers of robots and mines) are presented, and clear advantage over the greedy solution in both simple and more complex scenarios is demonstrated.