Competition for market leadership in game development seems to have reached a critical juncture. Game developers are to exceed expectations in achieving the balancing, keeping players engaged, and remaining profitable. Therefore, it’s critical that you find innovative ways to reduce costs, minimize risks and ensure content quality.
In order to remain profitable and stay competitive with increasing content demand, it’s important to ensure the game’s quality matches the quality levels expected by players. In short, the perceived value of a game is directly related to its difficulty or complexity levels. When designing a game experience, a huge experience on the one hand and an unbiased approach, on the other hand, needed to achieve the desired level of difficulty so players do not abandon the game as a result. But is it that easy to reach it in reality? What is the efficiency level and how do you measure it?
That leaves you with the strategic mission of finding innovative, cost-efficient ways to improve games without compromising your competitive edge and profitability. One of these ways is AI Agents, which is the focus of this piece.
As we’ve discussed in previous articles, human playtesting has proven to be a costly, time-consuming, and inaccurate process that simply does not match the modern needs of game developers and designers.
For years now, artificial intelligence has proven to be a powerful tool for game development, and playtesting is no exception. With the employment of Deep Reinforcement Learning, there’s now the introduction of virtual or AI agents who automate the playtesting process for faster processing and feedback, efficient difficulty balance, crash testing, and predicting engagement.
As the demand and complexity of modern games are on the rise, AI agents can easily scale playtesting operations, enhancing the delivery of gameplay quality.
Creating AI agents stems from data. Data helps shape behaviors and learning streams so these virtual agents can specifically complete a task per its design. Each agent needs to manage a balancing act between skills and style, using metrics like efficiency and speed to ensure the game is optimized for gameplay as well as enjoyable.
AI agents play the crucial role of playing through the game or portions of it, exploring different behaviors to generate meaningful data to build the case of what developers and designers are looking for to improve the game. Whether it’s playing through an extensive sequence of actions, trying to level up, playing a scenario from start to finish, or completing a task in the shortest sequence of actions, AI agents are trained and delicately fine-tuned to interact with the game with the goal of guaranteeing a quality experience.
Another key aspect of AI agents is their usage in the creation of in-game characters so they can interact with real human players. The possibilities are virtually endless as AI agents can play a variety of roles, searching for specific rewards, achieving the best possible performance, perform human-like exploration in an open-world game, and more. It’s in those tasks that their true value is gleaned.
The employment of AI agents is relatively new yet there has been an algorithmic approach to gaming design and development for quite some years now. These intelligent agents have come to streamline an important part of the game design and development pipeline, striking a balance between skills and style.
All in all, the gaming industry, and business health overall, is greatly benefited by leveraging AI agents as the high accuracy they yield and at the speed they do so, allow game developers to both implement needed changes at break-neck speed and release new content for user consumption with high quality.