Ever since people have started taking gaming as an impactful source of entertainment, people have been looking for ways to scale the industry too. In 2023, Nvidia presented the use of NLP in making AI powered NPCs and showcased their ability to realistically communicate with other NPCs and the main character. It was a huge breakthrough and an aggressive introduction of AI in the game development industry.
However, the progress has been slow as a matter of fact you can say innovation in AI and games is currently drowsing. The reason is the first phase of development; focuses on building an ecosystem that can sustain AI powered solutions’ substantial computing power. Once we have those ecosystems maybe we will be able to keep churning solutions for game development more conveniently. But what are those solutions? Well, the most interesting one so far is using Deep Learning in game development. The area is quite explored, but people don’t know how well it elevates user experience.
On the other hand there is a regression model which is traditional or you can say the fundamental of integrating deep learning in game development. However, we are here to answer the “what if” and “why”. Through comparison we will learn about both Deep learning and Regression Models, however, our final statement will unveil which one is the best when it comes to game development.
Understanding the Position of Regression Model and Deep Learning in Game Development
In a regression model, deep learning and game development data is a crucial compost. Even though there is trash, waste, irrelevant, or misleading information, still respected industries need to capitalize on that data. The more data they churn and the more data sets they form, performance and user experience elevate. Regression testing ensures the integrity of these models as the learning cycle starts again.
Regression Model in Game Development
To grasp the concept of a regression model for games, we need to understand the relationship between users and subscriptions. Subscription models are one of the easy ways to monetize your games and in return your user gets exciting rewards. The model focuses on the worst outcome and you can adapt on the basis of that, in the subscription model it is losing subscribers.
Condition:
Nearly 30% of your game platform users are not buying using the benefits of your subscription model.
Problem:
Soon they will stop using your subscription model in the future for every purpose.
Suggestions :
- Reduce the prices of the in game assets.
- Start an event for engagement ratio.
- Increase community influence.
Such suggestions are highly impactful sure, but they require a roadmap and that is complex to fabricate.
Regression Model
- Definition: Establishing a relation between a dependent variable and an independent variable for various purposes.
Use case in games
In a game there are many variables, some are dependent ones others are independent.
- Dependent variables: User’s rank and personality score.
- Independent variables: User’s mechanical and tactical skills.
- Purpose: Statistically establishing a relationship between them provides you with numerous purposes for improving your game in different ways.
- Forecasting: Identifying patterns in players’ behavioral scores can help developers update content and features to maximize player satisfaction.
- Price optimization: Reassess pricing options by understanding the historical data of players. Better for strategizing countering players’ spending and such.
- Recommendation: Better recommendations to players in terms of gaming assets, from the lens color of character to even their full body avatar it include everything.
- Risk Assessment: Now risk assessment can be a huge area to explore. From legal risk to technical risk it encompasses everything and a regression model can help identify, analyze, and mitigate these risks.
So in conclusion what regression does is it leverages your game’s historical data and generates predictions. Let’s easily understand it using a simple example:
Historical data: Players Engagement Time + Spending Limit.
Prediction 1: The player is likely to purchase a new battle pass.
Prediction 2: The player is likely to spend on new skins.
Prediction 3: Player likely to book a seat at real life esports of your game.
This is just an example, a game is filled with multiple data and a plethora of “conditional averages”. A multivariate regression is an efficient way to find out average conditions benefiting your game developer and company’s goal. However, the regression model soon evolved, although few people did state that this is new artificial intelligence. It was more of a budding stage, the blooming phase came after introducing machine learning and its subset deep learning.
Deep learning
Deep learning is a subfield of Artificial intelligence. It utilizes a vast amount of data sets, learns from them, and provides us with accurate output. The field has been effectively used to understand and eradicate real-world problems. There is a clear correlation between real world problems and deep learning, you see the field of technology is inspired by the human brain.
The human brain tends to solve problems through learning, understanding, and acting. Similarly a deep learning system will learn the data through utilizing artificial neural networks. One can find multiple and overlapping correlations between data said as data patterns. A DL system is willing to learn those patterns, find other significant but distinctive correlations and patterns to elevate the end result.
Let me simplify by stating the characteristics of Deep Learning
Data-drive: The system needs to be exposed to more data if it wants to keep increasing the resulting grade.
Learning curve: DL systems can learn complex relationships between data.
The Hierarchy: Every output produced is a data set too for the system to learn, improve, and increase the performance power.Non-Linearity: A data may not have linear connections between the variables. As deep learning is inspired by neural networking, it has the ability to learn those data sets too.
Deep Learning in Game Development
Deep Learning functions were invented in 2012. Since then they have been polished, reworked, and dedicated to various kinds of operations and industry. One of the best use cases can be seen in a booming industry we are witnessing today; game development. Deep learning at first used to work on decision trees; that is what makes your NPC chat with your player using dialogue boxes. But that was a bit too mechanically synthesized. The voices were not satisfying the user’s experience, this is when the DL function managed to get a hold on statistical, decision based, and a bit of an open hidden state method.
The fundamental of every deep learning function is to use neural networks, learning, combining creating and learning again. This cycle goes on until we are satisfied with the result.
For Example, think of your Game:
- Content A – a characteristic of a main character, i.e. its ability to jump.
- Content B – being the character’s ability to attack.
- So the result (after going through DL function) – A character can jump kick, attack the opponent and land on the ground.
- Here there is also this hidden state, “an auxiliary output”. In terms of the game it’s a “side effect’. Your character will land on the surface and quickly adapt to a combat stance. (Just like Mr. Miyagi’s crane stance).
Now all this is achieved through one particular and principal resource data. So sure DL functions have been present in the scene for quite some time now but data made it more popular. The rise of the quote “ Data being the next oil” is true and can be applied to the game industry too.
Another significant feature is when it learns from data, the whole operation eliminates the possibility of human intervention. If you can predict the chances of making fewer errors for a game, from the developers’ perspective you increase the probability of nearly perfecting the experience for users.
Human interference is not a bad thing, your user base craves authenticity. However, putting your resources to work and still not being able to predict the success of the outcome is the gray area. You are to experience major setbacks in terms of time-cost and soon long term scalability constraints.
Deep learning works on millions and billions of parameters giving you the desired output plus something like a hidden state. It’s easy to say yet complex to understand. Considering you have a data pool, utilizing that data pool to train a deep neural network model is not as easy as it seems. But once you train the model, you work with these high dimension input-output spaces. Again the complexity is difficult to understand but the benefits are easy to reap. These terms and use cases will be more clear and effective too, once we start integrating deep learning into game development.
Real World Examine In Game Development
So basically what connects both game development and deep learning is huge data sets. In deep learning, you train huge data sets using high computational power. These are not just simple data sets, we have high dimensional input data and high dimensional output data. Every data is dependent on infinite possibilities, hence making it independent too. A deep learning function works with these high dimensional data and is continuously evolving at the same time.
Basics
Assume you have a domain X and a Range Y. To train the function, first simply you need to map the route for the function to learn; it’s called ‘Mapping’. Now in game the domain X can be anything for example a player’s in-game actions and choices. The range of Y can span across billions and millions of possibilities, hence we need supervised learning methods.
The Problem that Deep Learning Solves
A limited data range is like a storage unit right just like your in game asset library. You have a limited amount of assets in the library, if you want to use them to make a character you combine those assets and get a final result. Here you are the medium between the output and input, a function works the same. In deep learning between input and output data sets the function is like a person looking up to the library and finding the best result for you.
Although such an iterative process surprisingly works, if the range of Y and X is not decided, here the process reaches its limitation. The limitation is quite simple: the process repeats itself, it does not learn and keeps on copy pasting. In game these domain ranges are users and their conditional averages and for unique user experience you cannot repeat the process, or the content too. To simply understand it let’s take a neuron operation and interpret it in terms of game development, but just for character choices.
- Neuron Operation Function:
zj=w1jx1+w2jx2+…+wnjxn+bj
yj=f(zj)
Where f is an activation function.
And here, each neuron j is an artificial neural network (ANN).
yj – Output
x1,x2,…,xn – Inputs
w1j,w2j,…,wnj – corresponding weights
bj – bias term.
- In terms of game development, assume your character needs to make decisions on various inputs. Each input is a piece of data. Considering a 1v1 combat game, your character needs to attack. It’s a simple action that may lead to a particular reward. So in that case applying the function we get.
zj=w1jx1+w2jx2+…+wnjxn+bj
yj=f(zj)
Where,
xi – Input features related to the decision making process.
wij – Weights representing the importance of each input feature.
bj – Bias towards making decisions.
zj – Weight sum of inputs.
f(z) – Activation function determining the neuron’s output.
The inputs: Punch, distance between you and enemy, health, attack power, type, counter. And the basic flowchart can be considered as given below:
State-Action-Reward is a generalized mapping technique that uses mathematical data. However, experts trying to discover paradigms of AI in game development learned that the inputs are not limited to mathematical equations. The inputs we are talking about are voice, gesture, biometrics, image, controllers, sensory, etc. By generating a byproduct for learning and also learning from its previous inputs and outputs, deep learning can help game developers achieve unique user experience standards.
Use case of Deep Learning in Game Development
In Particular, there are three best use cases when you integrate deep learning in game development. Those are Content Creation obviously, User Interface, and Game AI.
Content Creation
Utilizing pre trained neural networks and functions to create content that is completely new and fresh. For example: if you are creating an RPG game environment and need characters that are combat veterans, you can utilize a pre-trained neural network of combat veterans and integrate it to get results. Again here instead of equations you need to have inputs such as depth, velocity, geometrical size and shapes, and many more things to produce countless perfect iterations.
UI and UX
Esports players often complain about language barriers being a huge issue while communicating with their teammates. If players are aiming for the big bucket of prizes, they don’t want to hinder their boot camps, scrims, and major events due to the language barrier. Hence deep learning has the ability to recognize speech, learn the language, and apply real time translation to eliminate that barrier. These translations are realistic not your average mechanical AI assistant voice (no offense siri).
Game AI
An adaptive, random, and completely unique yet life-like NPCs are the first pick for developers to test their AI integration capabilities. Both Machine learning and deep learning are used to adapt to users’ behavior, strategies, skillsets, etc and generate realistic character movements or responses. Here are the following functions Game AI brings to the table:
- NPC behavior: Reacting to player, unexpected conversations with player, combat and environment interactions.
- Enemy AI: Adapting to player behavior, learning from them, and countering their skills.
- Game world simulator: Dynamic environment like crowd control, weather patterns, animal behavior, etc.
- Procedural content generation: Creating side quests and character specific challenges as the game progresses.
People usually question why I should play a game with AI. What’s fun about that? Well it’s not actually. It’s about playing with a bot that is nearly and realistically human. The choices, the reactions, and the actions are all human-like. Even the character avatar if you consider AR VR and metaverse games.
Conclusion
In 1956, few scholars wanted to discover if computers could understand and perform cognitive tasks, one of them being playing games. Since then we have come quite far, not only computers are able to play games, but integrating deep learning in game development can help developers build games.
Just like data with no structure leads to exploitation, Jumping into deep learning without proper understanding can be a big mess. If you are looking to utilize the features of Artificial intelligence like deep learning in game development, consider hiring an expert. An AI game development company with experience in game development too. They will have the resources, the experts, and the right methodologies to develop your idea successfully.
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