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https://github.com/linkdd/aitoolkit
Give a brain to your game's NPCs
https://github.com/linkdd/aitoolkit
ai behavior-tree cpp cpp23 finite-state-machine gamedev gamedev-library goal-oriented-action-planning utility-ai
Last synced: about 6 hours ago
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Give a brain to your game's NPCs
- Host: GitHub
- URL: https://github.com/linkdd/aitoolkit
- Owner: linkdd
- License: mit
- Created: 2024-01-08T11:33:43.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2024-05-08T14:18:53.000Z (8 months ago)
- Last Synced: 2024-12-22T01:07:59.971Z (7 days ago)
- Topics: ai, behavior-tree, cpp, cpp23, finite-state-machine, gamedev, gamedev-library, goal-oriented-action-planning, utility-ai
- Language: C++
- Homepage: https://linkdd.github.io/aitoolkit/
- Size: 1.27 MB
- Stars: 469
- Watchers: 6
- Forks: 25
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE.txt
Awesome Lists containing this project
README
# AI Toolkit
**AI Toolkit** is a header-only C++ library which provides tools for building
the brain of your game's NPCs.
It provides:
- Finite State Machines
- Behavior Tree
- Utility AI
- Goal Oriented Action Planning
Why this project? Well, I wrote about it [here](https://david-delassus.medium.com/ai-toolkit-give-a-brain-to-your-npcs-a-header-only-c-library-02a50ae9faed?sk=011cd1ed8e61d22f1be6b6430847f430).
## Installation
Add the `include` folder of this repository to your include paths.
Or add it as a submodule:
```
$ git submodule add https://github.com/linkdd/aitoolkit.git
$ g++ -std=c++23 -Iaitoolkit/include main.cpp -o mygame
```
> **NB:** This library is compatible with C++20.
Or using [Shipp](https://github.com/linkdd/shipp), add it to your dependencies:
```json
{
"name": "myproject",
"version": "0.1.0",
"dependencies": [
{
"name": "aitoolkit",
"url": "https://github.com/linkdd/aitoolkit.git",
"version": "v0.5.1"
}
]
}
```
## Usage
### Finite State Machine
First, include the header:
```cpp
#include
using namespace aitoolkit::fsm;
```
Then, create your blackboard type:
```cpp
struct blackboard_type {
// ...
};
```
Then, create a state type for each of your states:
```cpp
class state_dummy final : public state {
public:
virtual void enter(blackboard_type& blackboard) override {
// ...
}
virtual void exit(blackboard_type& blackboard) override {
// ...
}
virtual void pause(blackboard_type& blackboard) override {
// ...
}
virtual void resume(blackboard_type& blackboard) override {
// ...
}
virtual void update(blackboard_type& blackboard) override {
// ...
}
};
```
Create your simple state machine:
```cpp
auto simple_bb = blackboard_type{};
auto simple_fsm = simple_machine();
simple_fsm.set_state(state_dummy{}, simple_bb);
simple_fsm.pause(simple_bb);
simple_fsm.resume(simple_bb);
simple_fsm.update(simple_bb);
```
Or with a stack state machine:
```cpp
auto stack_bb = blackboard_type{};
auto stack_fsm = stack_machine{};
stack_fsm.push_state(state_dummy{}, stack_bb);
stack_fsm.push_state(state_dummy{}, stack_bb);
stack_fsm.update(stack_bb);
stack_fsm.pop_state(stack_bb);
stack_fsm.pop_state(stack_bb);
```
### Behavior Tree
First, include the header:
```cpp
#include
using namespace aitoolkit::bt;
```
Then, create your blackboard type:
```cpp
struct blackboard_type {
// ...
};
```
Then, create your tree:
```cpp
auto tree = seq(
node_list(
check([](const blackboard_type& bb) {
// check some condition
return true;
}),
task([](blackboard_type& bb) {
// perform some action
return execution_state::success;
})
)
);
```
Finally, evaluate it:
```cpp
auto blackboard = blackboard_type{
// ...
};
auto state = tree.evaluate(blackboard);
```
For more informations, consult the
[documentation](https://linkdd.github.io/aitoolkit/group__behtree.html).
### Utility AI
First, include the header file:
```cpp
#include
using namespace aitoolkit::utility;
```
Then, create a blackboard type:
```cpp
struct blackboard_type {
int food{0};
int wood{0};
int stone{0};
int gold{0};
};
```
Next, create a class for each action that you want to be able to perform:
```cpp
class collect_food final : public action {
public:
virtual float score(const blackboard_type& blackboard) const override {
return 50.0f;
}
virtual void apply(blackboard_type& blackboard) const override {
blackboard.food += 1;
}
};
class collect_wood final : public action {
public:
virtual float score(const blackboard_type& blackboard) const override {
return 150.0f;
}
virtual void apply(blackboard_type& blackboard) const override {
blackboard.wood += 1;
}
};
class collect_stone final : public action {
public:
virtual float score(const blackboard_type& blackboard) const override {
return -10.0f;
}
virtual void apply(blackboard_type& blackboard) const override {
blackboard.stone += 1;
}
};
class collect_gold final : public action {
public:
virtual float score(const blackboard_type& blackboard) const override {
return 75.0f;
}
virtual void apply(blackboard_type& blackboard) const override {
blackboard.gold += 1;
}
};
```
Finally, create an evaluator and run it:
```cpp
auto evaluator = evaluator(
action_list(
collect_food{},
collect_wood{},
collect_stone{},
collect_gold{}
)
);
auto blackboard = blackboard_type{};
evaluator.run(blackboard);
```
### Goal Oriented Action Planning
First, include the header file:
```cpp
#include
using namespace aitoolkit::goap;
```
Then, create a blackboard class that will hold the state of the planner:
```cpp
struct blackboard_type {
bool has_axe{false};
int wood{0};
};
```
> **NB:** The blackboard needs to be comparable (`a == b`) and hashable.
Next, create a class for each action that you want to be able to perform:
```cpp
class get_axe final : public action {
public:
virtual float cost(const blackboard_type& blackboard) const override {
return 1.0f;
}
virtual bool check_preconditions(const blackboard_type& blackboard) const override {
return !blackboard.has_axe;
}
virtual void apply_effects(blackboard_type& blackboard, bool dry_run) const override {
blackboard.has_axe = true;
}
};
class chop_tree final : public action {
public:
virtual float cost(const blackboard_type& blackboard) const override {
return 1.0f;
}
virtual bool check_preconditions(const blackboard_type& blackboard) const override {
return blackboard.has_axe;
}
virtual void apply_effects(blackboard_type& blackboard, bool dry_run) const override {
blackboard.wood += 1;
}
};
```
Finally, create a plan and run it:
```cpp
auto initial = blackboard_type{};
auto goal = blackboard_type{
.has_axe = true,
.wood = 3
};
auto p = planner(
action_list(
get_axe{},
chop_tree{}
),
initial,
goal
);
auto blackboard = initial;
while (p) {
p.run_next(blackboard); // will mutate the blackboard
}
```
For more informations, consult the
[documentation](https://linkdd.github.io/aitoolkit/group__goap.html).
## Documentation
The documentation is available online [here](https://linkdd.github.io/aitoolkit).
You can build it locally using [doxygen](https://www.doxygen.nl/):
```
$ make docs
```
## License
This library is released under the terms of the [MIT License](./LICENSE.txt).