Adaptive AI for competitive gaming: particle-swarm-optimized neural network for skill, engagement, and strategic evolution

Artificial Intelligence (AI) has transformed the development of game agents, providing new levels of interactivity and engagement. Real-time decision-making, as in fighting games, abets the need for adaptive and human-like behaviour for agents, making competition difficult. In the classics of fighting games, traditional AI is based on pre-programmed scripts, rules-based systems, or approaches that are easily predictable and provide less engaging gameplay. This paper presents an Adaptive AI based on Particle Swarm Optimization (PSO) to adapt its strategies dynamically based on the opponent’s behaviour. The proposed approach enables constant real-time updates to neural network weights, thus making continuous learning, strategic adaptation, and variation in gameplay. The proposed AI is evaluated against multiple state-of-the-art AI models and human players with several performance metrics like Élő Rating, Glicko-2, opponent adaptation score, engagement score, and win consistency score. Experimental results show that the proposed Adaptive AI performs better than other AI in terms of its adaptability, strategic diversity, engagement, and the level of competitiveness it provides against human opponents, which is fair and challenging at the same time. From the findings, it is concluded that real-time optimization can be achieved by integrating PSO with neural networks, which helps improve capabilities in fighting games. The research brings value to the field by creating an adaptable AI agent that enhances user gameplay.