AIrium 2

Each creature is represented as a matrix of blocks in a 2D grid world

There are three kinds of blocks:

• neuron blocks
• action blocks
• sensor blocks

all signals exchanged by blocks are floating point numbers

TODO: energy system

Neuron Blocks

They act as connectors, they have a data direction attribute which tells in which direction their output flows

They can be represented as arrows

Action blocks

They take an input signal and act by doing something, for example:

• they move the body in space
• they eject something
• they change color
• they read the next block
• they write the next block
• they "eat" a block

Sensor blocks

They give adiacent blocks a signal, for example:

• they communicate how far is the first block in a straight line
• they communicate the amount of blocks in relation to blank spaces in an area
• they communicate the color/type/activity of the first block in a straight line
• they communicate information about the presence of a particular kind of block in a direction
• they communicate information about the creature itself, like integrity of adiacent blocks

Traversing the brain graph

In this system, the body is the brain. It can be represented either as a graph of arrows (neuron blocks) and nodes (action/sensor blocks) or as a matrix of block instances.

Graph traversing pseudocode:

stack = []

getOutput = function (avoidLoops) :
    if avoidLoops: return lastIterationCache
    if currentIterationCache: return currentIterationCache
    stack.add(thisBlock)
    outputs = []
    for each input block in this block:
        thatBlock = the current input block we're checking
        output.add(thatBlock.getOutput(amIInTheStack)) // passes avoidLoops as true to avoid cyclic structures
    res =  // use the outputs to figure out what to return
    lastIterationCache = currentIterationCache
    currentIterationCache = res
    return res

clearStacks()
// Clean up iteration
fixIterationCacheOfAllBlocks()

for each output block in the body:
    actionBlock.apply(actionBlock.getOutput())