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This Chapter contributes to the understanding of intentional cooperation in multi-robot systems. It is shown that in a complex multi-robot system comprising several individual agents, each of which capable of interacting with their surroundings, cooperation is achieved implicitly through mediation of independent decisions that are made autonomously. It is claimed that in order for a multi-robot system to accomplish the objective, it is necessary for the individuals to proactively contribute to planning, to incremental refinement, as well as to adaptation at the group-level as the state of the mission progresses. It is shown how the decomposition of a mission delegated to a multi-robot system provides the system with capability of distributed decision-making. While formulating decision mechanism, it is shown how state of every agent with respect to the mission is viewed as two mutually exclusive internal and external states. Furthermore, it is demonstrated that the requirement of a priori knowledge in decision process is prevented via incorporating a simple sub-ranking mechanism into the external state component of the robotic agent. While such independent involvement preserves autonomy of the agents, it is shown that the mediation of such independent decisions does not only ensure proper execution of the plan but serves as a basis for evolution of intentional cooperation among individuals. The chapter develops and analyzes systematic approaches for mission decomposition that are formulated on a robust mathematical basis, yet exhibit high flexibility and extendibility as per mission specifications. It demonstrates a novel decision mechanism based on subdivision of internal and external states of robot with respect to a delegated mission. This prevents the requirements of a priori knowledge for decision-making through a simple opportunistic ranking module. The chapter also studies the performance of the proposed decision mechanism in contrast to the Bayesian formulism to demonstrate the results of decision process in the absence of a priori information. The chapter also introduces two novel multi-robot coordination strategies capable of preserving group level optimality of resultant allocations throughout the operation, solely based on the independent decisions of individual agents. It examines the performance of these coordination strategies in comparison to the leader-follower, the prioritization, the instantaneous, and the time-extended allocation strategies to demonstrate the optimality of its allocation strategy.