Design of a Hyper-Casual Futsal Mobile Game Using a Machine-Learned AI Agent-Player

Abstract

Mobile games continue to gain popularity, and their revenues are increasing accordingly. However, due to the inherent constraints of small screen sizes and restrictions of computing, it has been considered challenging to simulate the complex gameplay of soccer games. To this end, this paper aims to design and develop a simplified version of a five vs. five hyper-casual futsal game with only three player positions: goalkeeper, striker, and defender. It also tests a demo game to verify whether it is possible to implement an AI agent-player for each position to machine-learn and to run on a mobile device. A demo game with an AI agent-player was simulated using both PPO and SAC algorithms, and the feasibility and stability of the algorithms were compared. The results showed that each AI agent-player achieved the assigned objectives for each position and successfully machine-learned. When the algorithms were compared, the SAC algorithm showed a more stable state than the PPO algorithm when SAC directed the gameplay and interactive AI techniques. This paper shows the great potential of the application of machine-learned AI agent-players for soccer simulators on mobile platforms.

Efficient Difficulty Level Balancing in Match-3 Puzzle Games: A Comparative Study of Proximal Policy Optimization and Soft Actor-Critic Algorithms

“…In particular, AI advancements in NPCs and level design are of great interest to the game industry as they directly influence the immersive gaming experience [4,5]. Consequently, research on how to enable AI to make precise and efficient situational judgments is steadily progressing [6][7][8]. In particular, the automatic generation of game stages with different difficulty levels was a focus of interest in applying AI.…”

Optimizing Stage Construction and Level Balancing of Match-3 Puzzle Game with PPO Algorithm Machine Learning

“…In the pursuit of developing an AI system for automated gameplay, the significance of machine learning (ML) cannot be overstated. It is essential for developers to carefully select a reinforcement learning algorithm that is most suitable for the specific game [9]. Nevertheless, determining the optimal algorithm can be a challenging task due to the diverse types of algorithms available in reinforcement learning.…”