Open AccessRobots as models of evolving systems
Author(s) -
Gao Wang,
Trung V. Phan,
Shengkai Li,
Jing Wang,
Yan Peng,
Guo Chen,
Junle Qu,
Daniel I. Goldman,
Simon A. Levin,
Kenneth J. Pienta,
Sarah R. Amend,
Robert H. Austin,
Liyu Liu
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2120019119
Subject(s) - robot , byte , biology , genetic diversity , state (computer science) , computer science , evolutionary biology , artificial intelligence , sociology , algorithm , population , demography , operating system
Significance We present a fully realized adaptive resource landscape with diploid three-gene robots presenting interacting roles of population dynamics, mutations, breeding, death, and birth. Although modeling and theory serves as a guide here, the inherent complexity of our robobiology world makes it an experiment in exploring rules of Darwinian natural selection at a level difficult to simulate. We find that the lower the genetic diversity, the lower the survival probability of the robot population. We propose that diploid gene robots can act as avatars of diploid mammalian cells to explore novel programs of administration of drugs.