Decision-making AI in digital games

Abstract: The field of artificial intelligence has gained much knowledge through the implementation of decision-making systems in video games. One of these systems was the Goal Oriented Action Planning system (GOAP) which directs the behavior of an AI-agent through multiple digital artifacts categorized as goals, actions, and plans. The aim of the thesis is to aid in the understanding and creation of GOAP driven AI-agents in a video game setting to promote research on this topic. The research question of this thesis was about finding out how the GOAP architecture compares to other video game decision-making systems. The theoretical framework introduces the concept of the illusion of intelligence in video games and presents a discussion focused on the different components which make up a GOAP system and other components that support it. Additionally, the theoretical framework explains the need for a comparison between different decision-making systems and explains the social impact of game AI research. The methods section introduces the criteria for the comparison between GOAP and other decision-making systems and presents a comparison process that was driven by a literature review. A GOAP system was designed for this thesis using the unified modeling language and concept maps. It was then implemented using C# code in a free-of-charge game engine called Unity. We present the pseudocode for the implementation of the GOAP system and show that this framework is a modular, customizable, and reusable system that enables AI-agents to create plans from a varied set of actions. Finally, the paper suggests further research within game decision-making AI and emphasizes the importance of game AI research for communities of game developers, hobbyists, and others who could benefit from game AI in their projects.