3 ms·

My first question would be, how exactly does the brain work? What inputs does it take, what outputs does it generate, what all is it capable of? My guess would

by jrowen 2y ago

My first question would be, how exactly does the brain work? What inputs does it take, what outputs does it generate, what all is it capable of? My guess would be that the brain is too simple and kind of locked in or highly limited. I think we need to start with modeling more basic things with as few built-in assumptions/structures as possible. It may take a long time before anything interesting develops in the simulation. Billions of years is impractical but I doubt we're gonna whittle that down to a span measured in days anytime soon. The ultimate challenge is how do you know that your system is capable of building/developing complexity, and how do you tell if it is making progress on that?

RaftPeople 2y ago
My goal was to limit how much I hard coded into the creatures function, so it could evolve as much as possible. This was the design: The environment was made of a small number of materials: 1-Non-life - blocks etc., obstructions 2-Plant-life - is food but not a creature, not mobile etc. 3-Creatures - alive and mobile (can be eaten by other creatures) Creatures: 1-Shaped like hexagons 2-Eyes: Located on one of the sides of the hexagon 3-Mouth: A medium length proboscis type thing on same side as eyes Touching plant or food with mouth=eating (which transfers energy) 4-Movement/Motor: Can move forward or backward (based on which way pointing) Can rotate to face new direction Special neurons connect to the motor+rotator 5-Senses - Eyes: Each eye had 32 rays of "light" that spread out in an expanding pattern Each ray provided instant information Information returned per ray: distance the ray traveled before hitting something, and type of material it hit Each ray within each eye connected to a group of input neurons so distance and material could be input 6-Senses - Sound: Each creature emitted waves of sound in decreasing amplitude in a full circle, like ripples in a pond This signal traveled faster than creatures top speed but much slower than instant Sound sensor neurons covered the entire "skin" of the creature 7-Senses - Body or Mouth touching other materials: The mouth and entire skin had direct touch sensor neurons which detected the type of material being touched Just like the eyes, if non-life material was being interacted with then one neuron fired, if plant then a different neuron, etc. 8-Senses - Body - being eaten: The entire skin had special sensors for when another creatures mouth is physically touching this creature (meaning it's being eaten) 9-Brain: Randomly created, then evolved per generation Many random/semi-random/ajustable factors controlled the higher level structure of the NN, examples: Number of layers Number of sections per layer (section=a group neurons more tightly connected intra-section compared to inter-section) Percent connectivity within section Percent connectivity between sections Percent conectivity between layers Percent of recurrent connetions (for each of the above) Type of neurons (in total or per section) Types of synapes (experimented with various things here) etc. 10-Fitness function Basically amount of energy at end. Creature starts with X, eating adds to it, moving reduces it, getting eaten reduces it I needed to tweak this a bit because the naive version rewarded limited movement. The creatures needed to evolve the ability to use their eyes, their motors, to respond to being eaten by another creature, to eat another creature (just by touchig with mouth). During the early generations they would just be jiggling around randomly, but eventually some would settle in to more productive movements. I noticed that for long running sessions (7 days), the best creatures frequently had a NN structure that was deep-ish, like 6 layers, and came together in middle layers with many signals whittled down to a much smaller number of neurons before exploding out again to connect to the control neurons.

by jrowen 2y ago

RaftPeople 2y ago

My goal was to limit how much I hard coded into the creatures function, so it could evolve as much as possible. This was the design: The environment was made of a small number of materials: 1-Non-life - blocks etc., obstructions 2-Plant-life - is food but not a creature, not mobile etc. 3-Creatures - alive and mobile (can be eaten by other creatures) Creatures: 1-Shaped like hexagons 2-Eyes: Located on one of the sides of the hexagon 3-Mouth: A medium length proboscis type thing on same side as eyes Touching plant or food with mouth=eating (which transfers energy) 4-Movement/Motor: Can move forward or backward (based on which way pointing) Can rotate to face new direction Special neurons connect to the motor+rotator 5-Senses - Eyes: Each eye had 32 rays of "light" that spread out in an expanding pattern Each ray provided instant information Information returned per ray: distance the ray traveled before hitting something, and type of material it hit Each ray within each eye connected to a group of input neurons so distance and material could be input 6-Senses - Sound: Each creature emitted waves of sound in decreasing amplitude in a full circle, like ripples in a pond This signal traveled faster than creatures top speed but much slower than instant Sound sensor neurons covered the entire "skin" of the creature 7-Senses - Body or Mouth touching other materials: The mouth and entire skin had direct touch sensor neurons which detected the type of material being touched Just like the eyes, if non-life material was being interacted with then one neuron fired, if plant then a different neuron, etc. 8-Senses - Body - being eaten: The entire skin had special sensors for when another creatures mouth is physically touching this creature (meaning it's being eaten) 9-Brain: Randomly created, then evolved per generation Many random/semi-random/ajustable factors controlled the higher level structure of the NN, examples: Number of layers Number of sections per layer (section=a group neurons more tightly connected intra-section compared to inter-section) Percent connectivity within section Percent connectivity between sections Percent conectivity between layers Percent of recurrent connetions (for each of the above) Type of neurons (in total or per section) Types of synapes (experimented with various things here) etc. 10-Fitness function Basically amount of energy at end. Creature starts with X, eating adds to it, moving reduces it, getting eaten reduces it I needed to tweak this a bit because the naive version rewarded limited movement. The creatures needed to evolve the ability to use their eyes, their motors, to respond to being eaten by another creature, to eat another creature (just by touchig with mouth). During the early generations they would just be jiggling around randomly, but eventually some would settle in to more productive movements. I noticed that for long running sessions (7 days), the best creatures frequently had a NN structure that was deep-ish, like 6 layers, and came together in middle layers with many signals whittled down to a much smaller number of neurons before exploding out again to connect to the control neurons.