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AI Use Cases

A collection of over 250 uses for artificial intelligence

A continually updated list exploring how different types of AI are used across various industries and AI disciplines,including generative AI use cases, banking AI use cases, AI use cases in healthcare, AI use cases in government, AI use cases in insurance, and more

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Dynamic difficulty adjustment in games

Introduction

The use of AI and ML has changed the face of many sectors, and the gaming industry is no exception. Another area that has been greatly influenced by AI is the Dynamic Difficulty Adjustment (DDA), which is a feature commonly used in video games to adapt the difficulty of the game to the player’s skills. This integration of AI has enabled the creation of games that are more dynamic, adaptive, and immersive. The most prominent example of this is Gen AI, which is the state-of-the-art AI technology that has helped to make this integration possible.

Challenges

Before the implementation of AI in DDA, there were certain issues that the gaming industry had to deal with. The first challenge was how to design a game that would appeal to both new players as well as the experienced ones. With DDA being absent, games tended to be either too challenging for new players and too simple for veterans which reduced the players’ interest and enjoyment of the game. Another problem was the fixedness of the games. This was because with DDA, the difficulty of the games was not adjustable and this made the gaming experience boring and foreseeable. Also, the different difficulty levels had to be developed by the game developers themselves, which was quite tedious and resulted into discrepancies in the game.

AI Solutions

This has been done by AI and especially the latest Generation AI. In real-time, Gen AI uses machine learning algorithms to understand the player’s movements and abilities and modifies the challenge based on the player’s progress. For example, if a player is having a hard time with a specific level, the AI will adjust the game and make it less challenging for the player to complete and if a player is doing through a level very quickly the AI will adjust the game and make it more challenging. Such dynamic adjustment of difficulty makes sure that the game remains interesting and competitive for every player. Also, Gen AI can also help in generating various difficulty levels of the game which would have been time consuming and expensive to create.

Benefits

There are a vast number of benefits of utilizing Gen AI in DDA and the following are some of them. To the players the major advantage is to have a more engaging and enjoyable gaming experience. This is because the game accommodates the players’ skill level hence they are not likely to quit the game easily. As for the game developers, the advantages are that they can reduce the time and effort needed for development, since the AI can generate the various difficulty levels automatically. Also, with the help of AI, the developers can store and process player’s data which will be a great tool to understand what part of the game needs improvement. Other advantages are improved player retention and loyalty as well as the potential of generating more revenue for game developers.

Return on Investment

It is possible to claim that the ROI of implementing Gen AI in DDA is high. Although, investment in AI could be quite costly at the initial stage, the returns are sustainable in the long run. This paper shows that AI has the potential of boosting game revenue through improving player engagement and retention. Also, the reproduction process of various difficulty levels can be simplified by AI, thus cutting down on costs. In addition, the information collected on players will enable the developers to come up with better games that will be successful in the market hence increasing revenue. Therefore, the return on investment of implementing Gen AI in DDA can be assessed as an enhanced player satisfaction, cost-effective development, and higher game revenue.

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