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NEWS

13 September 2022

With the advent of processors and the progress of data science (particularly computer vision and machine learning techniques), non-human actors have been trained to compete and, in an increasing number of cases, win against their homo sapiens counterparts. The benefits of creating AI players are not just about designing a bot that might invent new gaming strategies or be a fun opponent to play against. On the contrary, training such models gives us deep insights into the modern science of algorithms

Throughout history of human societies games have played various roles, be it ritualistic, educational or more recently, entertaining. No matter the genre, format, or ultimate function, they always have had one thing in common: playing them involved us, humans. Creating an artificial actor that could imitate real players for years was only a theoretical challenge- an imaginative exercise that occupied the minds of scholars such as famed Alan Turing, who in 40’s and 50’s worked on a chess algorithm using just pen and paper. However, thanks to breakthrough research taking place in the last several decades such concepts are no longer hypothetical. With the advent of processors and the progress of data science (particularly computer vision and machine learning techniques), non-human actors have been trained to compete and, in an increasing number of cases, win against their homo sapiens counterparts. The problem at hand is not by any means restricted to the realm of offline or online entertainment.  The benefits of creating AI players are not just about designing a bot that might invent new gaming strategies or be a fun opponent to play against. On the contrary, training such models gives us deep insights into the modern science of algorithms. It also proves that the reinforcement learning approach, a foundation of AI, really works and might be applied to other fields of the economy and technology. How did this revolution come about? Let’s take a broad look at the major stages that have led to the current state of AI in games and gaming.

Reinforcement learning (RL) in gaming: the basics

Over the last few years, AI has shown to be very effective in mastering various human-created games. The most popular technique used for creating models that can adopt rules provided and participate in a game is reinforcement learning (RL). Simply put, RL is a technique that lets the computer play its moves over and over and figure out how to be good at it, without strictly introducing the rules of the game and relying only on the information whether the tried strategy turned out to be successful or not. This approach resembles a situation where somebody figures out the rules of the game by observation only. It usually starts with a computer taking completely random actions and analyzing their effectiveness ex-post. The aim of that is to allow the algorithm to explore many different strategies and have some initial knowledge on which moves seem to be desired and which are not. Over time however the chance of making a random move gradually decreases. But what does it mean when the move is not random in the first place? When the AI makes a move, the algorithm calculates which decision seems to be the best at a given moment. The calculations are made based on the experience the model has gained so far (the more games the AI model has played, the more accurate the results should be). The not random option is about choosing the action which seems to be best according to the calculations – it allows the model to evaluate the best strategy it has learned.  On the other hand, the goal of making random moves, even in the late stage of training the model, is to allow it to explore new strategies. The latter may turn out to be the preferred ones.

AI vs thousands of years old games

This technique can be used to analyze any game that has rules- it applies to video, sport, card or board games equally. When it comes to classic games, the most famous achievements of modern computer science regard Go and chess.

Games are very complex and have a reach history, which initially posed a huge challenge for AI researchers and engineers. However, in the last decade scientists created reinforced learning engines that turned out to be up for the given tasks. In just 2 years AI managed to beat world class champions in both chess (2015) and Go (2017) respectively. The latter one became a major event in countries such as China and Korea and it’s speculated that the shock that the unexpected win by the computer over human players has led there to an increase in spending on AI related to AI R&D and education.

Computer killed the videogame star

Things get even more interesting in the case of the video games. In 2013, a new algorithm was released. Its features allowed it not only to play games on the Atari console, but to do so with very good results.

OTHER ARTICLES

21 June 2022

The IDEAS NCBR’s working group that will deal with research in the field of computer vision will be headed by habilitated doctor engineer Tomasz Trzciński, professor at the Warsaw University of Technology and at the Jagiellonian University. The research agenda of the group will focus on issues related to the effectiveness of artificial intelligence models both in the context of the accuracy and pace of computations, as well as resources necessary for their operation.

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