Game development today is increasingly reliant on machine learning (ML), regardless of the genre. Just think of popular video games like Red Dead Redemption where non-player characters interact with players and react to specifics surrounding player appearance and actions. But machine learning in gaming stretches beyond RPG and FPS games. Games such as chess or checkers are some of the earliest examples where machine learning played a pivotal role in game development.

Online casinos are no strangers to the power of AI and gamification. Players today can find tailored promotions, personalised dashboards, and much more all thanks to the use of machine learning and AI. Most recently, machine learning is revolutionising the development of game shows and trivia games, such as the ever-popular Who Wants To Be A Millionaire? trivia game, now available at a selection of new casinos, that harness the power of machine learning.

Participating in this exhilarating game show allows players to base their answers using elimination tools such as 50/50, Ask the Audience, and Ask the Host. Like everything else in our lives, this game show has undergone significant changes brought about by AI. But, when we talk about machine learning being implemented in trivia games, we aren’t talking about competing against it but how it improves the game experience for the player. Let’s see how machine learning is transforming the classic game we know and love.

Question generation is one of the key aspects behind trivia games like Who Wants To Be A Millionaire?. Thanks to AI and ML a variety of challenges are addressed. A large part of machine learning is driven by the computational power of these systems. Here’s a rundown of several components tackled by machine learning and algorithms:

Data processing: Games run on sophisticated algorithms that can intake, sort, and categorise amounts of data so massive that it’s hard to grasp fully, and they can do it quickly, too.

Extraction and indexing: Using the data gathered, specific datasets are extracted and indexed to easily identify opportunities for relevant question generation.

Contextual relevance: Algorithms proceed to organise datasets according to relevance, meaning whether the facts gathered are relevant to the current status quo.

Recognition of patterns: Here, the functionality of algorithms is twofold. Firstly, by finding specific patterns within datasets gathered and secondly, by finding patterns based on already answered questions and users’ responses to questions asked.

Figuration of relevance: By constantly gauging the engagement and patterns of users, algorithms are able to adapt questions based on factual information gathered and presented, and how users interacted with this information. This is paramount in continuously developing questions that are engaging and relevant to the audience.

The involvement of machine learning in trivia games allows an unprecedented level of customisation of questions. Machine learning can steer the game to customise it to the players’ specific likes, which makes it more fun and rewarding!

By scratching the surface of the subject, one can quickly conclude that digital entertainment venues such as online casinos understand the value that machine learning can bring to trivia games. It is therefore only logical that these companies are planning their business ventures with an eye on the future. In early 2024, Playtech signed a two-year extension with Sony Pictures Television (producers of Who Wants To Be A Millionaire?) to continue developing games based on the iconic show. The newest addition, Video Poker Live, is just the first in a string of Millionaire-branded games that are going to be rolled out. Machine learning is essential to the logistical network required to develop these games. It is the key component for making them as exciting and varied as the individual people playing them.

Needless to say, the value found in using machine learning and AI in video and casino games is unsurmountable. Not only can we look forward to an overall increased level of enjoyment, but a customised gaming experience tailored to the individual.

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