The object of research is a game AI and architectural solutions for its development. Game AI is one of the most complicated parts of game development, and it needs good tools to reduce complexity and speed up the development. But there is a lack of lightweight solutions, which can be rapidly implemented, provide the desired flexibility and reduce complexity. The article compares game AI and academic AI, and why standard academic AI techniques can’t be broadly applied to game development. Academic AI focuses only on getting the right results and designing optimal agents. Whereas game AI focuses on the player experience and seemingly intelligent agents, where perceived intelligence is more important than real level of intelligence. Considerable attention is paid to examine existing alternatives such as GAIA, SOAR and AI.Implant, their advantages and disadvantages. GAIA is the best fit for a game AI: it is reusable and very flexible, but it is a heavy solution for implementation from scratch. SOAR is interesting, but better fits for academic AI. The third analog – AI.Implant used to be a good solution a while ago, but now lacks support and documentation, and now is outdated. The proposed solution for a lightweight agent-based game ai architecture is described in detail with use cases. Provided architecture consists of 3 layers: Perception, Decision Making and Action Execution. The role of Perception is to gather information about the surroundings. Decision making is responsible for choosing what action to take depending on the information provided by Perception. Action Execution Layer is responsible for scheduling, managing and executing actions. The use case demonstrates each layer in action with two distinct agents: an infantryman and a sniper. Also the solution provides a space for improvement and extension, which can be useful for more complex applications, than described in the article The aim of the research is to reduce complexity and speed up the development of game AI with a lightweight solution.
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