The term machine learning is nothing but a subset of Artificial Intelligence which endeavours to provide the requisites for the systems to automatically learn and improve themselves out of the experience, not relying on any human intervention i.e., not needing to be programmed. 

In short, it is a computer program which would be able to access the data by itself and learn everything by itself too. This artificial intelligence, when instilled as a computer program, can even better human intelligence, especially in gaming. Not so long ago, we have seen how a super computer called Deep Blue succeeded in defeating the likes of former world Chess Champions like Gary Kasparov and Viswanathan Anand. Kasparov was quoted as saying “As long as a machine can operate in the perimeter knowing what the final goal is, even if this is the only piece of information, that’s enough for machines to reach the level that is impossible for humans to compete.” When a chess genius can say so, should we even question the ability of artificial intelligence, more importantly in gaming? 

How is machine learning being done?

Machine learning is a very clever exercise made possible by the study and collection of data based on players’ behaviour. This includes counting of wins and estimating the probability of wins, the possible action by the opponents in a particular stage like fold, raise, etc. (in case of rummy card game) in addition to estimating how long a player will play, till which level they can go and how much money he/she will spend and so on. All these historic back data are used to impart artificial intelligence in online gaming. This is done through certain algorithms which make it possible for the computer to react in a given situation. These algorithms aids in predicting the future outcomes and the patterns based on the user data. So much so, even gaming addictions can be found and prevented by artificial intelligence by warning the players when the pattern of their play tends to go in an unhealthy direction. The researchers made it possible by developing an online machine learning called “Random forests.” 

Types of machine learning:

There are four types of machine learning viz, supervised, unsupervised, semi-supervised and Reinforcement learning. The first one is, as the name implies, done backed up by historical data where an outcome is known. The second one does not have any past data and has to figure what is being shown on its own. The third one uses both labelled and unlabelled data with methods like classification, regression and prediction. The last one is what’s mostly used in online gaming industry which uses algorithms based on trial and error method.

An example of Rising popularity of machine learning in India:

The popularity of online gaming industry is too high in India that it prompted three engineering drop out youths viz, Prasade, Subhan Mishra and Harikrishna Ramesh to come forward and launch an AI platform called Norah AI under their company called Absentia virtual Reality. In fact, Absentia, even before its official launch, had signed eight clients, which has amassed Rs.2.5 crores in revenues. Having partnered with various gaming studios they have offered them pre-Beta data version of their tools too. In fact, Absentia can revolutionise the gaming industry by quick creation and incorporation of all game elements. In short, the game developers can create new games very quickly, due to machine learning.  

Machine learning in digital games

The machine learning has really uplifted the status of the online games in India. This helps gaming sites to understand the need of the customers and offer them personalised gaming experience. For example, it is an unwritten Rummy game rules that the users must be acquainted with everything that they need to know to play the game and enjoy it to the maximum. There is another game called “Crossy road” which requires a chicken to cross the road gaining a point when it hits a gift and losing a point when hits a truck. Running a trial and error of hitting gifts and trucks with equal intensity, the best tactics was found to enable the chicken to sail through the game with God like power. So much so that the data arrived out of digital race games like The open racing stimulator are used for virtual training of autonomous cars. 

Summing up, it provides exactly what a gamer wants based on the past preferences and by predicting the future outcome correctly it is able to balance things and give them a personalised experience. It is just a matter of time for the game developers to create a fresh game nowadays. No wonder why machine learning has succeeded in giving a facelift to the digital gaming in India.