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Designing an intelligent situated agent is a difficult task as the designer must see the problem from the agent's standpoint considering all its sensors and actuators. We have devised a co-evolutionary/reinforcement learning hybrid method to automate the design of hierarchical behavior-based systems. In our approach, the design problem is decomposed into two separate parts: developing a repertoire of behaviors and organizing those behaviors in a structure. Mathematical formulation shows how to decompose the value of the structure to simpler components. These components can be estimated and used to find the optimal organization of behaviors during the agent's lifetime. Moreover, a novel co-evolutionary mechanism is suggested that evolves each type of behavior separately in their own genetic pool. Our method is applied to the decentralized multi-robot object lifting task which results in human-competitive performance.