How Artificial Intelligence and Machine Learning can turbocharge a Game Developer’s career

6 min read

Gaming – whether board games or games set in the virtual realm – has been a massively popular form of entertainment since time immemorial. In the pursuit of creating more sophisticated, thrilling, and intelligent games, game developers have delved into ML and AI technologies to fuel innovation in the gaming sphere. The gaming domain is the ideal experimentation bed for evolving technologies because not only do they put up complex and challenging problems for ML and AI to solve, they also pose as a ground for creativity – a meeting ground for machine learning and the art of interaction.

Machine Learning and Artificial Intelligence in Gaming

The reliance on AI for gaming is not a recent development. In fact, it dates back to 1949, when the famous cryptographer and mathematician Claude Shannon made his musings public about how a supercomputer could be made to master Chess. Then again, in 1952, a graduate student in the UK developed an AI that could play tic-tac-toe with ultimate perfection.

AI in Chess

Source : Medium

However, it isn’t just ML and AI that are progressing through experimentations on games. Game development, too, has benefited a great deal from these pioneering technologies. AI and ML have helped enhance the gaming experience on many grounds such as gaming design, the interactive quotient, as well as the inner functionalities of games.

The above mentioned AI use cases focus on two primary things: one is to impart enhanced realism in virtual gaming environment and the second is to create a more naturalistic interface between the gaming environment and the players. As of now, the focus of game developers, data scientists, and ML researchers lies in two specific categories of the gaming domain – games of perfect information and games of imperfect information. In games of perfect information, a player is aware of all the aspects of the game throughout the playing session, whereas, in games of imperfect information, players are oblivious to specific aspects of the game.

When it comes to games of perfect information such as Chess and Go, AI has shown various instances of overpowering human intelligence. Back in 1997, IBM’s Deep Blue successfully defeated world Chess champion, Garry Kasparov in a six-game match. In 2016, Google’s AlphaGo emerged as the victor in a Go match scoring 4-1 after defeating South Korean Go champion, Lee Sedol. One of the most advanced chess AIs developed yet, Stockfish, uses a combination of advanced heuristics and brute force to compute numeric values for each and every move in a specific position in Chess. It also effectively eliminates bad moves using the Alpha-beta pruning search algorithm.

While the progress and contribution of AI and ML to the field of games of perfect information is laudable, researchers are now intrigued by games of imperfect information. Games of imperfect information offer much more challenging situations that are essentially difficult for machines to learn and master. Thus, the next evolution in the world of gaming will be to create spontaneous gaming environment using AI technology, in which developers will build only the gaming environment and its mechanics instead of creating a game with pre-programmed/scripted plots. In such a scenario, the AI will have to confront and solve spontaneous challenges with personalized scenarios generated on the spot.

Games like StarCraft and StarCraft II have stirred up massive interest among game researchers and developers. In these games, the players are only partially aware of the gaming aspects and the game is largely determined not just by the AI moves and the previous state of the game, but also by the moves of other players. Since in these games you will have little knowledge about your rival’s moves, you have to take decisions on the go and your moves have to be spontaneous.

The recent win of OpenAI Five over amateur human players in Dota2 is a good case in point. OpenAI Five is a team of five neural networks that leverages an advanced version of Proximal Policy Optimization and uses a separate LSTM to learn identifiable strategies. The progress of OpenAI Five shows that even without human data, reinforcement learning can facilitate long-term planning, thus, allowing us to make further progress in the games of imperfect information.

Career in Game Development With ML and AI

As ML and AI continue to penetrate the gaming industry, it is creating a huge demand for talented and skilled game developers who are well-versed in these technologies. Today, game development is at a place where it’s no longer necessary to build games using time-consuming manual techniques. ML and AI have made the task of game developers easier as by leveraging these technologies, they can design and build innovative gaming environment, and test them automatically.

The integration of AI and ML in the gaming domain is giving birth to new job positions like Gameplay Software Engineer (AI), Gameplay Programmer (AI), and Game Security Data Scientist, to name a few.

The salaries of traditional game developers is in stark contrast with that of those having AI/ML skills. While the average salary of game developers is usually around $44,000, it can scale up to and over $1,20,000 if one possesses AI/ML skills.

Gameplay Engineer

Average salary – $73,000 – $1,16,000

Gameplay engineers are usually part of the core game dev team and are entrusted with the responsibility of enhancing the existing gameplay systems to enrich the player experience. Companies today demand for gameplay engineers who are proficient in C/C++ and well-versed with AI/ML technologies.

Gameplay Programmer

Average salary – $98,000 – $1,49,000

Gameplay programmers work in close collaboration with the production and design team to develop cutting edge features in the existing and upcoming gameplay systems. Programming skills are a must and knowledge of AI/ML technologies is an added bonus.

Game Security Data Scientist

Average salary – $73,000 – $1,06,000

The role of a gameplay security data scientist is to combine both security and data science approaches to detect anomalies and fraudulent behavior in games. This calls for a high degree of expertise in AI, ML, and other statistical methods.

With impressive salaries and exciting job opportunities cropping up fast in the game development sphere, the industry is attracting some major talent towards it. Game developers and software developers around the world are choosing the field due to the promises of rapid career growth. If you wish to bag better and more challenging roles in the domain of game development, you should definitely try and upskill your talent and knowledge base by mastering the fields of ML and AI.

Packt Publishing is the leading UK provider of Technology eBooks, Coding eBooks, Videos and Blogs; helping IT professionals to put software to work. It offers several books and videos on Game development with AI and machine learning. It’s never too late to learn new disciplines and expand your knowledge base. There are numerous online platforms that offer great artificial intelligent courses. The perk of learning from a registered online platform is that you can learn and grow at your own pace and according to your convenience. So, enroll yourself in one and spice up your career in game development!

About Author:

Abhinav RaiAbhinav Rai is the Data Analyst at UpGrad, an online education platform providing industry oriented programs in collaboration with world-class institutes, some of which are MICA, IIIT Bangalore, BITS and various industry leaders which include MakeMyTrip, Ola, Flipkart etc.


Read Next:

Best game engines for AI game development

Implementing Unity game engine and assets for 2D game development [Tutorial]

How to use arrays, lists, and dictionaries in Unity for 3D game development