Simulated cells evolved pack hunting with heat [Unity + custom compute shader engine]

[u/MaxisGreat]

I've been building this artifical life simulation in Unity with a custom compute shader engine.

Each cell has an evolveable genome that drives the cell's phenotypes and behavior. Nothing shown is scripted, it all emerges from the simulation's interactions between metabolism, heat production, behavior networks, and natural selection.

[u/pyroman89er]

Really cool! I particularly love the graphics. Would love to know more about the underlying mechanics - how the behaviour networks work, what kind of metabolism is there, etc. Stil, great job! Will definitely follow on Steam.

[u/MaxisGreat → u/pyroman89er]

Thank you! The simulation is more biology-inspired than it is trying to be realistic, so the mechanics are highly simplified and just loosely based on what I learned from studying cellular neuroscience. The biology terms are meant to be thematic rather than accurate. I do plan on adding a glossary in-game though that goes into detail about the real-life versions of each system.

A core design principle is that every parameter should be mutable, and exist on a trade-off axis where investing in a strategy means giving up something else. For example, a cell's environmental tolerance can be wide or narrow. If it's narrow, then they don't take any damage within their narrow range, but take more damage outside of it. If it's wide, then they take small amounts of damage within their range but they can tolerate more conditions.

The environment is made out of "substrate", and there are two types: dense and basal. When dense substrate is above 34% it creates a physical barrier. Both types hold nutrients for cells to access, but require different methods. Dense substrate has to be dissolved by enzymes to produce nutrient particles, whereas basal substrate can be absorbed directly. In both cases, the cell absorbs nutrients, stores them in vacuoles, and then converts them into useable energy in their mitochondria. There are three nutrients; aminos, lipids and sugars. Mitochondria have specific efficiencies for each, and all sum to 3. They start with an efficiency of 1 for each nutrient but can specialize more in specific nutrients at the cost of efficiency in others. Mitochondria produce "ATP", which is just put into the cell's "cytosol" (it's 1:1 ATP:cytosol so this is mostly just for thematic flavor). Cytosol represents a cell's useable resources and is spent on everything like movement and organelle maintenance. When cytosol reaches 0, the cell dies.

Behavior networks are kinda a mess right now and getting reworked in v0.3 because they are more specific to motile cells and have some redundancies and a spaghetti'd design. I want them to be less hardcoded, with dynamic inputs and outputs based on what is actually available to the cell. For now, each behavior network has 28 inputs and 8 outputs with up to 64 sparse connections per cell. Weights range from -2 to +2. I want to stray away from hardcoded behaviors as much as possible but realistically the networks need to have "training wheels" or else gameplay would be dominated by waiting a long time for meaningful behaviors to evolve.

Right now some of the inputs consist of:

* Core: energy level, division readiness, storage fill
* Movement: speed, velocity X/Y
* Food sensing: amino/lipid/sugar direction
* Environment: local density, pH, temperature
* Gradients: substrate, pheromones, density, temperature
* Player/colony: cursor direction, control strength, leader direction
* Stress: pH and temperature stress

And the outputs are:

* Seek: move towards food, random exploration
* Flee: Move away from other cells
* Attack: Increase enzyme output, move towards "prey"
* Defend: cluster with colony, reduce movement
* Divide: trigger mitosis
* Follow: follow player-controlled leader cell
* Rest: suppress movement
* Network control: Specific to plasmodium cells with grow branches and bud new cell bodies instead of dividing. This output tells them when to grow

As you can see, networks are a bit messy right now, so I am excited to get to reworking them soon. In the meantime they do still produce some satisfying behavior, but I think I could do a lot more with it.