Traditional approaches to designing multi-agent systems are offline, in simula-tion, and assume the presence of a global observer. Artificial Physics (AP) or physicomimetics (Spears and Gordon 1999) can be used to self-organize swarms of mobile robots into formations that move towards a goal. Using an offline ap-proach, we extend the AP framework to moving formations through obstacle fields. We provide important metrics of performance that allow us to (a) compare the utility of different generalized force laws in the artificial physics framework, (b) examine trade-offs between different metrics, and (c) provide a detailed method of comparison for future researchers in this area. In the online, real world, a global observer may be absent, performance feedback may be delayed or perturbed by noise, agents may only interact with their local neighbors, and only a subset of agents may experience any form of performance feed-back. Under these constraints, designing multi-agent systems is difficult. We present a novel approach called“Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios ” or DAEDALUS to address these issues, by mimickin
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