By Manish Pansari
The buzz around AI in the gaming industry has been intensifying for a while now. Both Data Science and AI have brought significant transformations to the gaming industry. These developments have not only enriched gaming experiences but also helped in reforming the design processes. The past two years have been particularly exciting for the Indian Gaming industry, thanks to the paradigm shift in the overall consumption patterns of tech-savvy youth and some promising technological developments.
Role of Data Science in Gaming
Data Science is crucial in enhancing and improving overall gaming performance and experience. It aids in analysing development strategies and optimising gaming efficiency. Data-Driven technologies enable developers to derive essential patterns, trends, and user behaviours from complex data, facilitating continuous monitoring of performance and user engagement. Machine Learning (ML) tools further assist in the development of predictive models for optimising game conditions. Retention, or the number of players returning to the game, is a vital aspect of maintaining gaming performance. By integrating analytics software such as GameAnalytics, Firebase or Mixpanel, developers gain insights into player engagement and event data, empowering them to make informed decisions and improve game performance. Continuous innovation and dedication are vital to delivering a fantastic user experience. User experience (UX) and user interface (UI) are also critical for gamers. Games like Call of Duty, GTA, Hitman, and Assassin’s Creed have amassed a large fan base due to their appealing storylines, UI, and high-quality graphics. Hence, it’s imperative to have a proper market strategy and research in place.
Different types of AI used in gaming
Various games deploy different types of AI to create realistic and captivating gameplay experiences. This ensures that gamers remain engaged. For example, a racing game will have a different type of AI than a shooting one. The most common AI technique in racing games is Rubber Banding. Rubber Banding is used to maintain the interest levels in the game and ensure the race remains competitive.
Another crucial AI technique is pathfinding, which enables non-player characters (NPCs) to navigate game worlds realistically. For instance, if an enemy is chasing the gamer, NPCs will assess obstacles and find alternative routes to reach the gamer. Game developers can adjust difficulty settings and implement different AI levels to challenge players—modes such as easy, medium, and hard offer varying levels of AI sophistication. Designing AI programming is a complex task, requiring consideration of various aspects that impact the overall gaming experience.
What’s easier to learn? Data Science or AI?
Learning data science involves working with datasets and leveraging complex data for decision-making. Tools like Flurry or FireBase can be used to make informed decisions and improve game performance, focusing on metrics such as retention, average revenue per user, daily active users, monthly active users, etc.
In contrast, Learning AI can be relatively more challenging due to the need to design complex logic and create an immersive game experience that feels natural rather than purely computer-controlled. One has to deal with state machines, different types of behaviours, interaction layers, etc that adds to the overall complexity. It is vital to implement AI correctly in your games. If not designed properly, the different types of algorithms implemented inside a match will not be a fun experience for the user. A strong AI provides an excellent replayability experience. Games likeDark Soul series and Sekiro games have a difficult AI to tackle, but players don’t get bored of it. They take this as a challenge. So having a strong AI in place is frustrating and, at the same time, enjoyable as well – driving hardcore gamers to get hooked on the games for hours and hours together, even though they do almost the same repetitive tasks.
Games demonstrating brilliant Data Science and AI applications
Several games, including Candy Crush, Clash of Clans, the Need for Speed series, Red Dead Redemption, Chess etc, have effectively utilised data science and AI to enhance gameplay experiences. For instance, Candy Crush underwent extensive data science and research to understand user behaviour, resulting in a game that excels in core mechanics, game design, and sound, ensuring user retention. Open-world games like Red Dead Redemption, Grand Theft Auto, and Watch Dogs feature impressive AI implementations, creating a believable, immersive experience that resonates with players.
The convergence of data science and AI has unlocked boundless possibilities in the gaming industry, transforming games into immersive worlds that challenge and captivate us. By harnessing data-driven insights and intelligent AI algorithms, developers can create personalised gaming experiences that resonate with players on a deeper level. As we venture into the future, it is crucial to remember that the true power lies in the hands of creative visionaries who wield this technology. With endless horizons ahead, where virtual and real converge, gaming becomes a canvas for extraordinary adventures and the realisation of our wildest dreams.
The author is senior vice president, business (DSML), Scaler