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In order to fulfill the true promise of decentralized, selforganizing intelligence, a major design problem has to be overcome. Designing the individual-level rules of behavior and interaction that will produce a desired collective pattern in a group of human or non-human agents is difficult because the group's aggregate-level behavior may not be easy to predict or infer from the individuals' rules. While the forward mapping from micro-rules to macro-behavior in self-organizing systems can be reconstructed using computational modeling techniques such as agent-based modeling, the inverse problem of finding micro-rules that produce interesting macro-behavior poses significant challenges, all the more as what constitutes “interesting” macro-behavior may not be known ahead of time. An exploratory design method is described in this paper. It relies on interactive evolution. We show how it can be used to discover new, “interesting” patterns of collective behavior when one does not know in advance what the system is capable of doing, a generic situation in the design of collective intelligent systems.