AGENT FOR LEARNING AND OPTIMIZATION EXECUTION

An autonomous agent maintains and updates an underlying model in a dynamic system. The autonomous agent receives minimum acceptable criteria from secondary users of the underlying model. The agent then compares current output samples of the model with the minimum acceptable criteria. If output samples do not meet the minimum acceptable criteria, then the agent formulates alternative model improvement actions and evaluates each alternative action by modeling rewards associated with it. The agent executes the alternative model improvement action having the highest reward.