bevior_tree
| Crates.io | bevior_tree |
| lib.rs | bevior_tree |
| version | |
| source | src |
| created_at | 2023-09-18 07:03:31.106074 |
| updated_at | 2024-12-10 07:29:59.450659 |
| description | Behavior tree plugin for Bevy. |
| homepage | |
| repository | https://github.com/hyranno/bevior_tree |
| max_upload_size | |
| id | 975618 |
| Cargo.toml error: | TOML parse error at line 18, column 1 | 18 | autolib = false | ^^^^^^^ unknown field `autolib`, expected one of `name`, `version`, `edition`, `authors`, `description`, `readme`, `license`, `repository`, `homepage`, `documentation`, `build`, `resolver`, `links`, `default-run`, `default_dash_run`, `rust-version`, `rust_dash_version`, `rust_version`, `license-file`, `license_dash_file`, `license_file`, `licenseFile`, `license_capital_file`, `forced-target`, `forced_dash_target`, `autobins`, `autotests`, `autoexamples`, `autobenches`, `publish`, `metadata`, `keywords`, `categories`, `exclude`, `include` |
| size | 0 |
(hyranno)
documentation
README
bevior_tree
bevior_tree is behavior tree plugin for Bevy.
See examples directory.
The chase.rs example is written for your first step.
Docs are available, too.
If you want to know about specific node, unit tests in the code might help.
This crate started with reference to seldom_state,
which is good for state machines.
Comparison
bevior_tree is not the only option for making game ai.
Also you don't have to choose only one.
Choose or combine them for your needs.
For example:
-
seldom_stateis implementation of state machine. Good for things that have rigid states, not limiting to ai. No good for lots of interconnected states, since it has too much transitions to add. -
big-brainis implementation of utility ai. Utility ai select next action by their utility (expected gain). Perhaps you can useForcedSelectorkind inbevior_tree::sequentialto do similar things.
Compatibility
| Bevy | bevior_tree |
|---|---|
| 0.15 | 0.7 |
| 0.14 | 0.6 |
| 0.13 | 0.5 |
| 0.12 | 0.4 |
| 0.11 | 0.1 - 0.3 |
License
bevior_tree is dual-licensed under MIT and Apache 2.0 at your option.
Contributing
Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.