In this blog, we will discuss what machine learning is, and how it is used in the video game industry and game development.
INTRODUCTION | AI In The Video Game Industry
Covering the basics, Machine Learning is a way for a program to have the ability to learn and grow from experience, automate model building with minimum human assistance. ML is a subset of Artificial Intelligence.
focusing on training machines how to take big datasets and perform repeated simulations to achieve desired output.
Applications of ML can be observe in our day to day lives. Areas such as social media recommendations, fraud detection, e-mail spam filtering, speech recognition find frequent use of ML to improve functioning.
VIDEO GAME DEVELOPMENT
According to Newzoo, 2020’s global games market will generate revenues of $159.3 billion, which is a healthy year-on-year growth rate of 9.3%. This rate is more than the total net worth of the movie and the television industry. With numbers like this it is easy to estimate the immense potential in the gaming world. AI In The Game Industry is an arduous challenge that involves numerous tasks – from designing characters and levels to programming.
AI In The Game Industry
Game developers essentially look for ML algorithms to address the following issues:
- Playing games against advanced BOT players
- Creating unique level designs
- Immersive and realistic systems
- Audio generation
- Improving overall gaming experiences
1. Playing with advanced BOTS and NPC’s
Right now, video games have pre-scripted NPC’s, but a machine learning-based NPC could allow you to play against less-predictable foes. These foes can also adjust their difficulty level. As you learn to play, your enemies could get smarter and respond in unique ways because they would respond to your gameplay.
2. Creating Unique level Designs
Another field that is beginning the use of ML is texture and shader creation. though, These technologies are getting significant improvement by advanced generative adversarial networks, or GAN. There are various examples of this; just do a search for Deepfakes.
Another great use of ML is on the spot creation of objects as you progress in a gaming environment, No Man’s Sky is an excellent example of this, a game where an infinite number of new worlds can be discovered, all generated on the fly as you explore.
3. Immersive and Realistic Systems
Unity is one of the most popular gaming engines, used by AAA companies and indie platforms alike. It is use to make 2D, 3D, VR and AR visualizations. The Unity Machine Learning Agents Toolkit is an open-source plugin which enable simulations to function as an environment for training reinforcement learning agents.
also, The agents can imitate human mechanics and gameplay style through hours of rigorous simulations. The agents can also be train using neural networking, imitation learning and other machine learning techniques.
4. Audio generation
Machine learning has already been involved for audio generation purposes by various games. Wavenet is a deep neural network for generating raw audio. It is developed by Deep Mind, and Long Short Term Memory (LSTM) models, and are a form of Recurrent Neural Networks (RNNs) used in deep learning.
WaveNet generates new samples from the original audio samples which can be use to make unique sounds for game mechanics. LSTM has a wide range of applications in Sequence-to-Sequence modelling tasks like speech-to-text recognition, Video Classification, and so on.
5. Improving Overall Gaming Experience
ML Models can intruct to identify the most suitable colour for each pixel to benefit small images that are zoom in on or maintain the quality of a close up shot.
Improving up-close visuals isn’t the only way machine learning can level-up games. Developers can use deep neural networks to highlight and amplify those activities which the player is habitual to, while increasing the reward satisfaction for them at the same time.
A good example of this is Nvidia partnering up with Remedy to help create facial animations in Quantum Break, which were 80% accurate by simply listening to audio lines.
CONCLUSION
Machine learning is still to show its full potential in the gaming world.Game developers have started to capture the essence of ML, to automate computer programmes with least human interactions. However, video game AIs are still in its dark ages, with more advancement to come soon in the upcoming years.
However, As ML advancements are steadily incorporate into game design, the need for ML experts enthusiastic about gaming will get the dream jobs to work in a gaming company and open up new ways to advance game mechanics.
written by: Atharva Parashar
reviewed by: Shivani yadav
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