This monograph investigates the modeling and control of large populations of agents, drawing parallels between biological systems like the immune system and artificial systems such as robots. The authors extend research from Biology, Robotics, and Hybrid Systems Theory, inspired by the modeling of reactive behavior in immune cell populations where each cell is an independent agent. They propose that there is no fundamental difference between naturally created agents (cells) and artificially created agents (robots), a concept further explored through a case study of a large robotic population. The book formulates optimal control strategies for maximizing robotic presence in a given region, applying the Minimum Principle for partial differential equations to this challenge.

This work is recognized as an outcome of multidisciplinary research, with the first author's PhD thesis forming its basis. The monograph was a runner-up for the Georges Giralt Award for the best European PhD thesis in Robotics, indicating a strong academic reception within the field. The approach of unifying biological and robotic agent modeling is presented as mutually beneficial for both disciplines and for the broader understanding of multi-agent systems. The text is likely to appeal to researchers and advanced students in robotics, artificial intelligence, and computational biology.