| Publisher | MorbidCamel |
|---|---|
| File size | 26.62MB |
| Number of files | 109 |
| Latest version | 1 |
| Latest release date | 2016-10-11 08:57:27 |
| First release date | 2016-10-11 08:57:27 |
| Supported Unity versions | 2018.4.2 or higher |
Decision Tree Toolkit provides you with a pre-built kit that you can use to build decision trees for your NPC and gameplay AI and is optimized for decision making speed.
Decision trees are especially powerful in scenarios where AI behaviors needs to adjust dynamically during gameplay and a must have for RPG and strategy games that will keep the player guessing. Decision trees are also very useful in FPS bot learning to dynamically learn a player's habits and therefore make it even more challenging for a player.
For decision tree learning an implementation of the ID3 (Iterative Dichotomiser 3) algorithm is used to generate a decision tree from a dataset.
The dataset is in the form of a SQLite database that you ship with your game and the toolkit provides a pre-built database that can be customized for your needs. You can also update the dataset dynamically and rebuild the decision trees to have dynamic intelligence in your game or to implement machine learning based on the players actions.
Decision trees are fast because they only have to scan the dataset once and then derive a tree from it.
The toolkit provides a way to build multiple decision trees from a single dataset using configurable outcomes and makes it easy to nest decision behaviors for more complex learning solutions.
Decision trees are especially powerful in scenarios where AI behaviors needs to adjust dynamically during gameplay and a must have for RPG and strategy games that will keep the player guessing. Decision trees are also very useful in FPS bot learning to dynamically learn a player's habits and therefore make it even more challenging for a player.
For decision tree learning an implementation of the ID3 (Iterative Dichotomiser 3) algorithm is used to generate a decision tree from a dataset.
The dataset is in the form of a SQLite database that you ship with your game and the toolkit provides a pre-built database that can be customized for your needs. You can also update the dataset dynamically and rebuild the decision trees to have dynamic intelligence in your game or to implement machine learning based on the players actions.
Decision trees are fast because they only have to scan the dataset once and then derive a tree from it.
The toolkit provides a way to build multiple decision trees from a single dataset using configurable outcomes and makes it easy to nest decision behaviors for more complex learning solutions.
