In vivo, in silico, in machina: Ants and Robots Balance Memory and Communication to Collectively Exploit Information
Ants balance the use of remembered private information and communicated public information to maximally exploit resources. This work determines how the strategy that best balances these two sources of information, and the performance of that best strategy, depend on the information in the distribution that is available to be exploited, and the number of ants in the colony. We answer this question by (1) measuring the rates at which ants foraging for seeds in manipulative field studies, (2) simulating ant foraging strategies and measuring resulting foraging performance, and (3) implementing foraging strategies as algorithms for search behaviors in teams of cooperatively searching robots.
KeywordsAnts Swarm robotics Agent based modeling Evolutionary algorithms
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