Game-based learning - teaching artificial intelligence to play Minecraft : a systematic literature review

Abstract

Artificial Intelligence (AI) are machines designed to think and behave as humans would. Taking AI and placing it into a virtual world they become known as AI agents which uses the knowledge it gained from training to perform tasks in the world. AI agents in the virtual world has only been able to perform a narrow set of tasks with specialised models in environments with limited complexity and diversity. A rich world that requires an agent to continuously learn from and adapt to a wide variety of open-ended tasks and use previously gained knowledge to determine the next course of action will render the agent incapable. In order to investigate the AI teaching methods applied to instruct the agent to perform basic tasks in Minecraft in order to identify which AI teaching methods will yield the best results, a systematic literature review was conducted by extracting 57 papers and identifying themes and sub-themes that suited AI agent training methods and functions. This was to discover wat AI training methods can be implemented to enable an agent to perform tasks in a complex and rich world, contributing to game-based learning. The study found that a well-integrated Reinforcement Learning (RL) method with an effective reward system equipped the agent with the necessary knowledge to be able to perform tasks on a more complex level. A list of unique methods was integrated with RL such as Newtonian Action Advice (NAA), Behavioural Cloning (BC), VideoPreTraining (VPT), human demonstrations, and natural language commands to achieve a certain goal. This meant that AI agents can be taught to perform open ended tasks in a complex environment by setting up a well thought out framework on how to teach the agent in various areas leading to the possibility to incorporate those teachings into the real world through gamebased learning.