# big-brain `big-brain` is a [Utility AI](https://en.wikipedia.org/wiki/Utility_system) library for games, built for the [Bevy Game Engine](https://bevyengine.org/) 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](https://docs.rs/big-brain) for more details. ### Features * Highly concurrent/parallelizable evaluation. * Integrates smoothly with Bevy. * Easy AI definition using idiomatic Rust builders. You don't have to be some genius to define behavior that _feels_ realistic to players. * High performance--supports hundreds of thousands of concurrent AIs. * Graceful degradation--can be configured such that the less frame time is available, the slower an AI might "seem", without dragging down framerates, by simply processing fewer events per tick. * Proven game AI model. * Low code overhead--you only define two types of application-dependent things, and everything else is building blocks! * Highly composable and reusable. * State machine-style continuous actions/behaviors. * Action cancellation. ### Example As a developer, you write application-dependent code to define [`Scorers`](#scorers) and [`Actions`](#actions), and then put it all together like building blocks, using [`Thinkers`](#thinkers) that will define the actual behavior. #### Scorers `Scorer`s are entities that look at the world and evaluate into [`Score`](scorers::Score) values. You can think of them as the "eyes" of the AI system. They're a highly-parallel way of being able to look at the `World` and use it to make some decisions later. ```rust use bevy::prelude::*; use big_brain::prelude::*; #[derive(Debug, Clone, Component)] pub struct 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); } } } ``` #### Actions `Action`s are the actual things your entities will _do_. They are connected to [`ActionState`](actions::ActionState)s that represent the current execution state of the state machine. ```rust use bevy::prelude::*; use big_brain::prelude::*; #[derive(Debug, Clone, Component)] pub struct 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; *state = ActionState::Success; } ActionState::Cancelled => { *state = ActionState::Failure; } _ => {} } } } } ``` #### Thinkers Finally, you can use it when define the [`Thinker`](thinker::Thinker), which you can attach as a regular Component: ```rust cmd.spawn().insert(Thirst::new(70.0, 2.0)).insert( Thinker::build() .picker(FirstToScore { threshold: 0.8 }) .when(Thirsty, Drink), ); ``` #### App Once all that's done, we just add our systems and off we go! ```rust App::new() .add_plugins(DefaultPlugins) .add_plugin(BigBrainPlugin) .add_startup_system(init_entities) .add_system(thirst_system) .add_system_to_stage(BigBrainStage::Actions, drink_action_system) .add_system_to_stage(BigBrainStage::Scorers, thirsty_scorer_system) .run(); ``` ### Contributing 1. Install the latest Rust toolchain (stable supported). 2. `cargo run --example thirst` 3. Happy hacking! ### License This project is licensed under [the Apache-2.0 License](LICENSE.md).