big-brain
is a Utility AI
library for games, built for the Bevy Game Engine
It lets you define complex, intricate AI behaviors for your entities based on their perception of the world. Definitions are heavily data-driven, using plain Rust, and you only need to program Scorers (entities that look at your game world and come up with a Score), and Actions (entities that perform actual behaviors upon the world). No other code is needed for actual AI behavior.
See the documentation for more details.
Example
First, you define actions and considerations, which are just plain old Bevy
Component
s and System
s.
Scorers
Scorers
s are entities that look at the world and evaluate into Score
values.
use bevy::prelude::*;
use big_brain::*;
#[derive(Debug, Clone)]
pub struct Thirsty;
impl Thirsty {
fn build() -> ThirstyBuilder {
ThirstyBuilder
}
}
#[derive(Debug, Clone)]
pub struct ThirstyBuilder;
impl ScorerBuilder for ThirstyBuilder {
fn build(&self, cmd: &mut Commands, scorer: Entity, _actor: Entity) {
cmd.entity(scorer).insert(Thirsty);
}
}
pub fn thirsty_scorer_system(
thirsts: Query<&Thirst>,
mut query: Query<(&Actor, &mut Score), With<Thirsty>>,
) {
for (Actor(actor), mut score) in query.iter_mut() {
if let Ok(thirst) = thirsts.get(*actor) {
score.set(thirst.thirst);
println!("Thirst: {}", thirst.thirst);
}
}
}
Actions
Action
s are the actual things your entities will do.
use bevy::prelude::*;
use big_brain::*;
#[derive(Debug, Clone)]
pub struct Drink;
impl Drink {
pub fn build() -> DrinkBuilder {
DrinkBuilder
}
}
#[derive(Debug, Clone)]
pub struct DrinkBuilder;
impl ActionBuilder for DrinkBuilder {
fn build(&self, cmd: &mut Commands, action: Entity, _actor: Entity) {
cmd.entity(action).insert(Drink);
}
}
fn drink_action_system(
mut thirsts: Query<&mut Thirst>,
mut query: Query<(&Actor, &mut ActionState), With<Drink>>,
) {
for (Actor(actor), mut state) in query.iter_mut() {
if let Ok(mut thirst) = thirsts.get_mut(*actor) {
match *state {
ActionState::Requested => {
thirst.thirst = 10.0;
println!("drank some water");
*state = ActionState::Success;
}
ActionState::Cancelled => {
*state = ActionState::Failure;
}
_ => {}
}
}
}
}
Thinker Definition
Finally, you can use it when define the Thinker
:
let actor = cmd.spawn().insert(Thirst::new(70.0, 2.0)).id();
let thinker = Thinker::build()
.picker(FirstToScore { threshold: 80.0 })
.when(Thirsty::build(), Drink::build())
.attach(&mut cmd, actor);
cmd.entity(actor).push_children(&[thinker]);
License
This project is licensed under the Parity License. Third-party contributions are licensed under Apache-2.0 and belong to their respective authors.
The Parity License is a copyleft license that, unlike the GPL family, allows you to license derivative and connected works under permissive licenses like MIT or Apache-2.0. It's free to use provided the work you do is freely available!
For proprietary use, please contact me, or just sponsor me on GitHub under the appropriate tier to acquire a proprietary-use license! This funding model helps me make my work sustainable and compensates me for the work it took to write this crate!