AI in Gaming: Redefines Gaming Experience and Evaluations

1. Non-Player Character (NCP)

AI-based games have revolutionized the development of NCPs. These characters can be controlled by real players to behave intelligently. This technology is powered by AI algorithms like machine learning models and neural networks. NCPs can analyze game situations and adapt human interaction to make decision-making skills close to each player’s dynamics. However, they are automated on pre-programmed rules that cannot be controlled by game players. Many game companies leveraged this technology to improve strategies through trial and error. A main example is Seed (EA).

2. AI Visuals and Upscaling

AI has enhanced visuals and upscaled deep learning techniques. AI upscaling analyzes low-resolution images and upscales them to higher resolution without losing the quality. The technology also works on classic games even on older hardware. The major player for this use case example is NVIDIA DLSS technology. Their DLSS technology employs upscaling in games to deliver high-resolution graphics for improved frame rates. This allows players to alter any scene within the game. 

3. Procedural Content Generation

Video games have multiple aspects like 3-D objects, characters, props, clothing, scenes, music, graphics, levels, maps, quests, weapons, and more. However, developing these assets is not an easy task. It requires huge investments and good developers. Using this generative model, developers can generate diverse worlds, and simplify the process of asset generation that excels to the user needs. For instance, OpenAI’s GPT-3 language model offered AI Dungeon 2 an innovative text-based adventure game model. This showcases that if you give relevant prompts to AI, it can generate unique storylines for gaming characters. 

4. Player Experience Modeling (PEM)

PEM is the latest AI trend in gaming for mathematically modelling gamers to anticipate their liking and dislikes of a game. This involves AI modelling that predicts and gets trained on players’ preferences and behavior. AI does this by machine learning techniques to analyze players’ data like gameplay style, actions, and reactions to game scenarios and conditions. After gathering and learning all the data, AI models adjust game elements and create a personalized experience by modifying game difficulty for the player’s profile. 

5. Data Extraction and Real-time Insights

Global gamers generate vast amounts of data which is used by machine learning algorithms to analyze. this analysis helps generate valuable insights into user behavior, game performance issues and trends. DemonWare, an online multiplayer game uses predictive data analytics to understand player behaviours and optimize game performance and resource allocation based on these insights.

6. Player Sentiment Analysis

AI-driven sentiment analysis utilizes natural language processing technology to evaluate player feedback. This feedback includes text data from reviews, forums, and social media, identifying key themes and sentiments. This data provides developers with insights into player preferences and areas for improvement. For example, Riot Games uses AI tools to analyze player discussions on League of Legends, guiding in-game updates and enhancements.

7. Cheat Detection For Multiplayer

Cheating in multiplayer games is a common challenge. This impacts negatively on player experience and gaming platforms. AI can be used to analyze players’ movement patterns and detect cheating. Popular online games like PUBG already use AI tool to combat the growing risks of cheating and ensure fair gameplay for all players.

8. Testing and Debugging

AI have advanced tools for game testing. It tests and debugs using automated algorithms to scan game code, identifying anomalies, potential bugs, and performance issues. As a result of using AI, it reduces time and resources, resulting in a polished final product.

9. Virtual Assistants

Modern games have in-game AI assistants. These AI agents use natural language processing to understand players’ queries and respond accordingly. Using the gathered information they provide relevant guidance that results in increasing user engagement and retention rate during gameplay.