Artificial intelligence has been no less than a blessing. This phenomenal technology caters to not one, not two but hundreds and thousands of applications in every possible field that one can think of.
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One such interesting and probably one of the most widespread applications of all is in the gaming industry. Be it racing games, shooting games, or strategy games, all have numerous features that are controlled by AI.
Though AI brings life to the game, what turns out to be attention-seeking is the fact that today video games are now being trained to study their own patterns so as to advance their own processes. All this throws light on how far we have come as far as Al is concerned.
Two major reasons as to why Artificial intelligence is used extensively by organizations within the gaming industry are- one, to save the budget on the game design and second, to upgrade the in-game experience.
AI in gaming
One of the major reasons of deploying AI in gaming is to model a human player to understand how the interaction with a game is experienced by individual players. Usually, we get to see the following tasks being accomplished when it comes to designing games using AI – start by progressing smart and human-like NPCs (Non Person Character) to improve interaction with gamers followed by predicting the human players’ actions that lead to enhanced game testing and game design. Next up would be to classify their behaviours to allow the personalization of the game. Lastly it is time to discover frequent patterns or orders of actions to regulate how a player performs in a game.
Today, companies are exploring new technologies and creative opportunities that they can enable for the future. No stone is left unturned when it comes to creating virtual worlds without a human designer that is needed to build every inch of those worlds from scratch. When AI is deployed in order to design video games, the main areas that are focused upon are – machine learning and, more specifically, reinforcement learning. Reinforcement learning is a type of machine learning. It talks about how an agent learns to behave in an environment by performing actions and getting feedback. In simple terms, this type of machine learning is similar to the way humans or even dogs learn in their early years wherein an action is taken, feedback is received and a conclusion is drawn based on the feedback whether the action was good or bad. […]
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