This paper outlines a novel method for Cooperative Behavioral Control of distributed heterogeneous autonomous systems, emulating the methods in which humans collaborate. This method allows autonomous systems to collaborate on tasks and mission goals, similar to how humans interact, and is effective and efficient for real-time resource allocation. The proposed method fundamentally reduces the required communication bandwidths by significantly decreasing the amount of data necessary for real-time information exchange between cooperating agents. This is done by creating a swarm to estimate the beliefs of the collective, and not on physical states which is usually done by classical approaches. In sharing core beliefs, a collective of heterogeneous agents can plan as an individual, inherently and naturally deconflicting the notion of cost and optimality.