Multi-agent systems must be engineered to ensure that desirable system-level properties will consistently emerge from the complex interactions of the underlying agents, while also guaranteeing that undesirable behavior will be suppressed. We present an Aspect-Oriented Programming (AOP) framework for modeling, visualizing and manipulating emergent structure in multi-agent systems. By encapsulating the macroscopic structure, we can identify undesirable patterns of behavior at a higher level of abstraction. The identification of such patterns allows us to implement a feedback loop to steer the behavior of the lower level agents towards actions favorable for the emergence of a reliable solution.AOP facilitates the modeling of the system-wide behavior, thus it serves as a valuable tool for building confidence that a given multi-agent system will consistently meet its requirements.

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A collection of agents, faced with multiple tasks to perform, must effectively map agents to tasks in order to perform the tasks quickly with limited wasted resources. We propose a decentralized control algorithm based on synchronized random number generators to enact a cooperative task auction among the agents. The algorithm finds probabilistically reasonable solutions in a few rounds of bidding. Additionally, as the length of the auction increases, the expectation of a tener solution increases. This algorithm is not intended to find the optimal solution; it finds a "'goad" solution with less computation and communication.

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