Learning Parametrised RoboCup Rescue Agent Behaviour Using an Evolutionary Algorithm

摘要

Although various methods have already been utilised in the RoboCup Rescue simulation project, we investigated a new approach and implemented self-organising agents without any central instance. Coordinated behaviour is achieved by using a task allocation system. The task allocation system supports an adjustable evaluation function, which gives the agents options on their behaviour. Weights for each evaluation function were evolved using an evolutionary algorithm. We additionally investigated different settings for the learning algorithm. We gained extraordinary high scores on deterministic simulation runs with reasonable acting agents.