Information transmission in collectively moving agents.

信息可以是任何内部自由度，例如疾病状态、知识等。事实上，集体移动的代理人可以是觅食领地动物或勘测机器人，寻找一些目标。相遇和覆盖统计对于了解信息何时是最佳的(机器人)或何时应该双值化(疾病传播)是必不可少的。个体的移动统计，例如漂移或相关的随机行走，对信息传输的效率有什么作用？如果代理共享的空间不同，会发生什么情况？领地机器人和部分重叠领地的动物？我们将使用关于有限区域中随机游走传播子的新的分析结果来处理两两相互作用的场景。从这些结果将有可能解释集体运动模型的结果，无论是动物还是机器人。

Information may be any internal degree of freedom, such asdisease status, knowledge, etc.. In fact the collectively movingagents can be foraging territorial animals or surveying robotssearching for some targets. Encounter and coverage statistics are essential to understandwhen information is optimal (robots) or when it should beminimized (disease spread) What is the role of movement statistics, e.g. drift or correlatedrandom walker, of the individuals on the efficiency ofinformation transfer? And what happens if the space that agentsshare is not the same, eg. territorial robots and animals withpartially overlapping territories? We will use the new analytic results on random walk propagatorsconfined in finite domains to tackle the pairwise interactionscenarios. And from those results it will be possible tointerpret the outcome of the collective movement models, eitheranimals or robots.