Multi-Agent Maze Exploration

Mazes have intrigued the human mind for thousands of years, and have been used to measure mental abilities of laboratory animals. In recent years mazes have been used to measure the artificial intelligence of robots by examining their ability to traverse mazes using maze exploration and solution algorithms. We use a simulation of a multi-agent system and show that it is beneficial to use a group of several robots in maze exploration. Based on Tarry’s behavioral algorithm we demonstrate that the group performance improves and becomes more robust as the number of robots in the group increases. In addition, simulation results yield that the amount of data transfer required for group coordination can be minimized to a small set of data items, which is independent of either the number of robots in the group or the maze size. Thus, our solution can be scaled up to mazes and/or groups of any size.