Macroscopic analysis of adaptive task allocation in robots

We describe a general mechanism for adaptation in multiagent systems in which agents modify their behavior based on their memory of past events. These behavior changes can be elicited by environmental dynamics or arise as response to the actions of other agents. The agents use memory to estimate the global state of the system from individual observations and adjust their actions accordingly. We also present a mathematical model of the dynamics of collective behavior in such systems and apply it to study adaptive task allocation in mobile robots. In this application, the robots’ task is to forage for Red or Green pucks (a robot can only forage for one puck type at a time). As it travels around the arena, a robot records observations of pucks and other robots, and uses these observations to compute the estimated density of each. If it finds there are not enough robots of a specific type, it may switch its foraging state to fill the gap. After a transient, we expect the number of robots in each foraging state to reflect the prevalence of each puck type in the environment. We modelled adaptive task allocation and studied the dynamics of the system for different transition rates between states. We find that for some rates lead to fast convergence times and a steady state solution.