By David M. Burton
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Balch. Cooperative multiagent robotic systems. In Artificial Intelligence and Mobile Robots. MIT Press, 1998. 2. T. Balch. Reward and diversity in multirobot foraging, In IJCAI-99 Workshop on Agents Learning About, From and With other Agents, 1999. 3. Tucker Balch. org. 4. Stanley L. Brue. Retrospectives: The law of diminishing returns. The Journal of Economic Perspectives, 7(3):185-192, 1993. 5. Phil R. Cohen and Hector J. Levesque. Teamwork. Nous, 25(4):487-512, 1991. 6. G. Dudek, M. Jenkin, and E.
Thus, this experiment simulated a total of 24,000 trials of 9 minute intervals. The simulated robots we studied were based on the same behaviors. The only software differences between the robots lay within their implementation of the previously described teamwork coordination behaviors. Each robot had three common behaviors: wander, acquire, and deliver. In the wander phase, the robots originated from a random initial position, and proceeded in a random walk until they detected a resource targeted for collection.
We describe scale up experiments in foraging and search domains that are characterized by resources that lend themselves to group conflicts. We find that interference and productivity are strongly negatively correlated in such domains, and use this metric to explain differences in productivity between all teams. We posit that in the absence of spatial conflicts, all teams should consistently demonstrate marginal gains during scale up. We confirm this idea by easing the "space crunch" in our domains and notice how all groups consistendy demonstrate marginal returns.
An introduction to abstract mathematical systems by David M. Burton