| One model of human learning involves choosing an action based on past experiences in similar situations. The chosen action is typically modified to compensate for any discrepancies with the current situation. After sufficient experience has been obtained, at least in a particular regime, the experiences are conceptualized into a general response mechanism. This thesis presents an algorithm that formalizes this hybrid reasoning process and applies it to control of a nonlinear physical system. Experiences are stored as vectors of variables known as cases in a set called a casebase. Vector norms are used to select an appropriate case from the casebase which is then modified using an adaptation routine. Once the modified action is applied to the system and the resulting outcome is observed, the casebase is augmented to include the new experience for improved future performance. A gated expert neural network is eventually trained on subsets of the casebase to create local inverse model approximations for regions of the input space where sufficient data is available to support generalization. The gate network selects one of the experts if appropriate or otherwise defaults back to casebased reasoning. The applicability of the hybrid algorithm is demonstrated on a nonlinear control problem, setpoint regulation in a ball and beam system. |
