Five AI tools every game developer should have in their toolbelt – European Gaming Industry News
The gaming industry is no stranger to artificial intelligence (AI) in video games. Anyone who has played an NPC populated game or multiplayer games with bots will know that the quality of the AI that defines how they interact with players can be extremely variable. Played well, the AI-controlled characters help a game to be more cohesive and engaging.
That’s why gaming AI research is a constant priority for many studios, with larger companies like Sony exploring advanced AI techniques like reinforcement learning, which has the potential to create the most advanced form of NPCs to be a player’s friend or foe, depending on how they play.
But the capabilities of AI don’t stop there. AI is also changing the way games are developed, adding much greater realism to in-game characters, the gaming experience, and creating autonomous online robots that are nearly indistinguishable from human players.
Electronic Arts is currently developing systems that use machine learning to reproduce facial expressions, skin types and body movements from videos and photos. In theory, that means actors would no longer have to walk into a mo-cap studio and there would be a greater range of genres and ethnicities produced in games.
With so much going on in this space, we’ve compiled a list of five AI tools every game developer should have on hand to make their life easier.
One of the most time-consuming processes a developer will face is content generation, especially in puzzle games where there is a high demand for new levels. But AI technology can help here, using a method known as procedural content generation (PCG), which algorithmically creates content. AI can be used to generate dozens or hundreds of variants ready for review and selection. If AI is used for level generation, it is even possible to use AI robots to play the levels and rate them for difficulty or the time it takes to complete them.
PCG enables faster content creation and gives developers more time for creative experimentation. AI can easily do the heavy lifting, but human input is always crucial to launch a great product, this is where the skills of the developer come into play.
Unlike mobile apps or web pages, games are one of the most difficult forms of software to test due to their great variation, the almost endless number of states a game can have, its patterns of operation. personalized interaction and its constant updates.
Games are still mostly tested by humans, which means the process can be long and expensive. While large studios have the budget to create dedicated teams that focus solely on game testing, smaller developers mostly rely on their network of friends, colleagues, and fans. But what happens when they are unable to test all aspects of the game? This is where AI and machine learning come in.
Testing in video games is complex because of the number of choices in a fraction of a second a player can make and the results of those choices. Additionally, there are interactions with other human and non-human players who also play the game non-deterministically, further complicating matters.
The advantage of using AI bots for your gaming tests is that they can test quickly, consistently, and methodically. For example, bots can be used to test things like clipping objects by performing all possible moves and interactions at a much faster level than a human player.
Cheating disables multiplayer games, even if they are not directly affected. For gamers who strive to be the best, it’s all too easy to turn to cheat tools that grant superhero powers in the game. Cheat tools range from simple aimbots that help aim and from shooting in FPS games like CounterStrike to mining robots in MMOs that control a player’s character and basically play for him or her.
Developers can combat the use of cheaters with machine learning and behavior-based detection AI. By collecting data about a player’s behavior patterns, such as how a player moves the controller or how quickly a player reacts, AI can recognize when an action goes beyond normal human behavior.
While there are many other products that can detect cheaters, the advantage of a machine learning system is that it is constantly learning and adapting to suit the game, making it more difficult to learn. deception for gamers. This means developers can use updates and fixes for the game without having to reprogram the AI.
Online toxicity has become a growing problem as more games are built around persistent online worlds with thousands of concurrent players. Sometimes it happens that a small number of players spoils the experience of the majority. However, AI is a great ally for developers looking to curb such behavior and protect their players.
There are different approaches to moderating game chat. The most common is to use lists of prohibited words and phrases, which can be used to block posts that use them. The problem here is that players can bypass moderation by changing letters to numbers or using slang phrases.
A better approach is to use an AI model that can take into account the context of the text so that even if a player is smart with the spelling of a word, it will still be flagged due to its context and sound. intention. This type of AI is relatively new, with only a handful of experts working in this area. But, in the absence of signs that toxic behaviors are decreasing and the number of people playing games is increasing, this is an area that will continue to need expert solutions.
While AI can capture the bulk of comments before they are posted, human moderators are still required as the ultimate arbiter of permitted content. But by working as a team, human moderators and AI moderation can together ensure that games are a safe space for all players.
Replacement AI players
The great attraction of online games is the ability to interact and team up with other players. However, there aren’t always enough human players to fill the empty seats; that’s where bots come in.
Bots can serve as a reasonable substitute for human gamers, with a sophisticated range of behaviors. But it’s still pretty obvious to most when playing alongside a bot rather than a real person.
One way to solve this problem is to use AI to introduce more variables into the behavior of robots. It can make for a much more realistic feel of unpredictability and replicate things like the risk-taking you often encounter with human players.
While the combination of techniques used to create effective AI-based robots is quite new, when done correctly it can create dynamic, adaptive, and very human robots that add an extra dimension to online play.
AI tools are there to help developers
Developers face an immense amount of challenges developing great games very quickly, but thanks to the latest AI-based tools, there are powerful solutions to some of the more resource-intensive aspects of the development process.
This is why AI is increasingly becoming an essential addition to the game development process, providing developers with the tools and information necessary to solve any problem that arises. This, in turn, allows them to offload tedious tasks and focus on creative production.
Based in Denmark, modl.ai is a team of game developers, engineers and AI experts working together to create AI-powered tools to create better games. Its AI-powered tools allow developers to quickly create and test games and understand their players. Model.ai’s unique technology is designed to speed up the game development process, automating repetitive and time-consuming tasks and helping developers improve and increase player engagement.
modl.ai was founded in 2018 by Christoffer Holmgård, Benedikte Mikkelsen, Lars Henriksen, Sebastian Risi, Georgios N. Yannakakis and Julian Togelius, who between them have been involved in the launch of over 30 games and have over 28,000 citations in technical literature covering AI and game design. In 2019, the company was successful in securing seed funding from a number of investors led by PreSeed Ventures, the largest and most successful start-up investor in Denmark, joined by Saltagen Ventures and Propagator Ventures.