The game of life represents a spatial environment of cells that live and die according to fixed rules of nature. In the basic variant of the game a cell’s behavior can be described as reactive and deterministic since...The game of life represents a spatial environment of cells that live and die according to fixed rules of nature. In the basic variant of the game a cell’s behavior can be described as reactive and deterministic since each cell’s transition from an actual state to a subsequent state is straight-forwardly defined by the rules. Furthermore, it can be shown that the alive cells’ spatial occupation share of the environment decreases quickly and levels out at a really small value (around 3%), virtually independent of the initial number of alive cells. In this study we will show that this occupation share can be strongly increased if alive cells become more active by making non-deterministic sacrificial decisions according to their individual positions. Furthermore, we applied signaling games in combination with reinforcement learning to show that results can be even more improved if cells learn to signal for navigating the behavior of neighbor cells. This result stresses the assumption that individual behavior and local communication supports the optimization of resourcing and constitute important steps in the evolution of creature and man.展开更多
文摘The game of life represents a spatial environment of cells that live and die according to fixed rules of nature. In the basic variant of the game a cell’s behavior can be described as reactive and deterministic since each cell’s transition from an actual state to a subsequent state is straight-forwardly defined by the rules. Furthermore, it can be shown that the alive cells’ spatial occupation share of the environment decreases quickly and levels out at a really small value (around 3%), virtually independent of the initial number of alive cells. In this study we will show that this occupation share can be strongly increased if alive cells become more active by making non-deterministic sacrificial decisions according to their individual positions. Furthermore, we applied signaling games in combination with reinforcement learning to show that results can be even more improved if cells learn to signal for navigating the behavior of neighbor cells. This result stresses the assumption that individual behavior and local communication supports the optimization of resourcing and constitute important steps in the evolution of creature and man.
