In earlier papers we presented a distributed model of action selection in an autonomous intelligent agent (Maes, 1989a, 1989b, 1991a, 1991b). An interesting feature of this algorithm is that it provides a handful of parameters that can be used to tune the action selection behavior of the algorithm. They make it possible, for example, to trade off goal-orientedness for data-orientedness, speed for quality, bias (inertia) for adaptivity, and so on. In this paper we report on an experiment we did in automating the tuning and run-time adaptation of these parameters. The same action selection model is used on a meta-level to select actions that alter the values of the parameters, so as to achieve the action selection behavior that is appropriate for the environment and task at hand.
