Reaction AI ReInvented

Enjoy the dynamic and action animation template, which includes a unique collection of Burning Elements!
This package contains 12 Burning Elements sprite sheet animations for 2D Games. Render pipeline compatibility The Built-in Render Pipeline is Unity’s default render pipeline. It is a general-purpose render pipeline that has limited options for customization. The Universal Render Pipeline (URP) is a Scriptable Render Pipeline that is quick and easy to customize, and lets you create optimized graphics across a wide range of platforms. The High Definition Render Pipeline (HDRP) is a Scriptable Render Pipeline that lets you create cutting-edge, high-fidelity graphics on high-end platforms. Unity Version Built-in URP HDRP 2021.3.0f1 Compatible Not compatible Not compatible Description Template was created and works correctly in version 2021.0.3 Features: Sprite animations work much better than any other shaders and particles due to memory capacity. Optimized for games. Pivot point set properly. No setup or plug-ins required. All materials, sprites and other parts of pack properly named. Demo Scene with all prefabs is included. Supported platforms: Windows Web GL iOS Android Mac OS X Linux How to use: Simply drag and drop the “Fire” prefab to your scene and use it. (Prefab(s) located under Assets> Burning Elements > Prefabs. Technical details File Formats: transparent Png. Each frame dimension is 512*288 px. 30 FPS Works with any render pipelines Related keywords Effects burning Action 2D Fire anime Platformer Dynamic Games particles elements flames VFX overlays Report this asset

• Dynamic Non-player character Combat Artificial Intelligence using Machine Learning Multiclass prediction engine (Reaction AI + Microsoft ML.NET)

• A* pathfinding

• Automatic audio detection and following for NPC

NOTE: requires: x64 platform and Unity version

Unity 2019.2.19f1 (64-bit) or higher

Built on top of Reaction AI Steroid Edition with optimizations and now using Microsoft ML .NET Machine Learning for Multiclass predictions for NPCs’ moves.

Still includes A* pathfinding as well as automatic follow sound source

see User Guide

Reaction AI is an artificial intelligence engine to be used for game developers that have nonplayer characters governed by AI or other game components that rely on artificial intelligence for reactions based on actions by the avatar. It can also be used for other systems that require learning systems and provides an intelligent output based on input.
Reaction AI models the human Mind and neural networks in its design. It learns over time and uses past events in a memory system to determine the next best reaction. However, it is pseudo-dynamic in the resulting output based on how well the “mind” is functioning at that time based on stimuli. The “mind” is divided into four components, each sized proportionally to the human mind.

Once there is enough data, it will train an ML .NET multiclassification model and use that model to make predictions of what the NPC action should be. The prediction is based on a feature vector that includes providing a parameter for the desired success value, and then vending the respective action based on that with the other features provided for the training and prediction.

If a model has been trained, it will be saved locally and used for further predictions. The model can continuously get trained with additional data over time. The training data is also saved locally and can be used to train the model upon making an API call. The training data size limit is also configurable and will not exceed the amount specified (to not allow the data size to increase beyond a desired amount).

The multiclass training and predictions use a Multiclass Maximum Entropy training algorithm.

Before the model is trained, the API will utilize the base Reaction AI algorithm for determining the NPC move. As mentioned, once the Machine Learning Model is trained, it will then start making prediction using the trained model. Furthermore, the predictions will also introduce a reaction using the base algorithm (not the model) once every 5 times as a means to introduce “noise” to allow the next iteration of training the model to be more dynamic.

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