2002 Volume 15 Issue 9 Pages 451-458
A distributed autonomous approach to adaptive system design is investigated through the Cooperative Behavior Acquisition Problem (CBAP) by a homogeneous autonomous robot group. A task is supposed to be given only to the group of robots, for the purpose of putting the main interest on how to design on-line specialization mechanisms, which should be autonomous and adaptive. The robot group dealt by this paper is comprised of same autonomous robots connected by a triangular board. Reinforcement learning (RL) is adopted for a basic framework of the robot's decision-making mechanism, so that quick online learning can be expected. However, since RL in a simple form is not effective in developing a stable cooperative behavior in a multi-agent environment, a novel decision-making mechanism is designed using two RL units, of which the first RL unit is for predicting its partners' next states, and the other is for generating an action of its own. Several empirical experiments for three connected robots are conducted on a computer in order to investigate the effectiveness of the proposed mechanisms.