AI can rival control theory for goal achievement in a challenging dynamical system

Nonlinear dynamical systems are notoriously difficult to control. The Acrobot is an under-actuated double pendulum in a gravitational field. Under most driving schemes the Acrobot exhibits chaotic behavior. But with careful applications of energy it is possible to gradually pump the system so as to swing it over its supporting joint. This swing-up task is of current interest to control theory researchers. Conventional notions of AI planning are not easily extended to domains with interacting continuously varying quantities. Such continuous domains are often dynamic; important properties change over time even when no action is taken. Noise and error propagation can preclude accurately characterizing the effects of actions or predicting the trajectory of an undisturbed system through time. A plan must be a conditional action policy or a control strategy that carefully nudges the system as it strays from a desired course. Automatically generating such plans or action strategies is the subject of this research. An AI system successfully learns to perform the swing-up task using an approach called explanation-based control (EBC). The approach combines a plausible qualitative domain theory with empirical observation. Results are in some respects superior to the known control theory strategies. Of particular importance to AI is EBC's notion of a "plan" or "strategy" and its method for automatic synthesis. Experimental evidence confirms EBC's ability and generality.