Marc Atkin and Paul R. Cohen
Many tasks require an agent to monitor its environment, but little is known about appropriate monitoring strategies to use in particular situations. Our approach is to learn good monitoring strategies with a genetic programming algorithm. To this end, we have developed a simple agent programming language in which we represent monitoring strategies as programs that control a simulated robot, and a simulator in which the programs can be evaluated. The effect of different environments and tasks is determined experimentally; changing features of the environment will change which strategies are learned. The correspondence can then be analyzed.