Game of Intelligent Life

Overall, we explore the possibility of emergent behaviors in a multi-agent setting with a simple goal of predicting the next state of the game. By giving each cell agency with a convolutional neural network, we were able to explore more complex multi-agent behavior, giving each agent a short term memory in the form of convolutional parameters, and the ability to explore vs exploit their environment through their output movements. The goal was to explore the types of emerging behavior from groups of CNN agents with a selective pressure toward better predictions of the next state. Question In an environment where pixel agents are given mechanisms to self-replicate, compete, communicate, and predict, are these channels enough for the emergence of centralized control from distinct agents? Related Work This work was heavily inspired by the paper "Growing Neural Cellular Automata" 1, as well as by the original Game of Life by John Conway 2. The ResNet architecture was also borrowed and modified from this tutorial 3. Finally, the idea of a fitness value for the cells comes from the field of genetic algorithms. The guiding question and following philosophical implications are deeply connected to the works of Deleuze and Simondon among other philosophers and physicists. Assumptions Macroscopic wholes can emerge from discretized atomic agents Intelligent, accurate predictions of the world emerge from resource scarcity, thus a system requiring accurate predictions to grow imposes resource constraints correlated to those which life imposes on cell division and growth. We can model primordial selves with self replicating simplified agents given arbitrary, nonstationary bounds on themselves in the form of a pixel.