In the last part of this work, we proposed and evaluated a novel online-learning based approach for developing recovery policies. The system constructs runtime estimates for the likelihood of success of each recovery strategy, together with confidence bounds for those estimates. These estimates are then used to construct a policy online, while balancing the system's exploration and exploitation goals. Experiments with a deployed spoken dialog system showed that the system was able to learn a more effective recovery policy in a relatively short time period.
