Resilient swarm behaviors via online evolution and behavior fusion

Abstract

Grammatical evolution can be used to learn bio-inspired solutions to many distributed multiagent tasks, but the programs learned by the agents often need to be resilient to perturbations in the world. Biological inspiration from bacteria suggests that ongoing evolution can enable resilience, but traditional grammatical evolution algorithms learn too slowly to mimic rapid evolution because they utilize only vertical, parent-to-child genetic variation. The BeTr-GEESE grammatical evolution algorithm presented in this paper creates agents that use both vertical and lateral gene transfer to rapidly learn programs that perform one step in a multi-step problem even though the programs cannot perform all required subtasks. This paper shows that BeTr-GEESE can use online evolution to produce resilient collective behaviors on two goal-oriented spatial tasks, foraging and nest maintenance, in the presence of different types of perturbation. The paper then explores when and why BeTr-GEESE succeeds, emphasizing two potentially generalizable properties: modularity and locality. Modular programs enable real-time lateral transfer, leading to resilience. Locality means that the appropriate phenotypic behaviors are local to specific regions of the world (spatial locality) and that recently useful behaviors are likely to be useful again shortly (temporal locality). Finally, the paper modifies BeTr-GEESE to perform behavior fusion across multiple modular behaviors using activator and repressed conditions so that a fixed (non-evolving) population of heterogeneous agents is resilient to perturbations.