As a little project, I'm working on building out a neural network that learns to play a snake game. The game is composed of a max 20x20 board where each cell is either an apple, a snake, or a wall. One way I thought of encoding that data in the input layer would be to have a 20x20 400 neuron input layer where each neuron has one of 3 different states signaled by neuron value. Say, 0 apple, 0.5 snake, 1 wall. Is encoding multiple states in a single neuron like this a good idea?

Representing biology textbook knowledge involves handling numerous concepts that have multiple possible states, for example, developmental states such as embryo, juvenile and larva; system states such as homeostasis and equilibrium; states of chromosomes such as chromatin, nicked, etc. Though substantial research exists on formalisms for representing states, relatively less work exists on ontologically representing them in a complex domain. Our findings include: (a) the word state in natural language is used with both entities and events which requires that we generalize the traditional definition of state to distinguish between an entity state and an event state; (b) an abstract modeling pattern called the process flow diagram that provides a practically achievable target for the output of natural language processing programs, and enables knowledge authoring by domain experts that can be compiled into a well-known background theory based on action languages. The background theory, combined with reasoning methods from the action language, allows building tools that simulate processes and answer sophisticated questions about process interruptions.