| Real-time interactive systems require decision-making mechanisms that simultaneously ensure high behavioral quality and low response latency. The choice of artificial intelligence architecture critically affects system responsiveness, scalability, and the realism of autonomous agent behavior. The paper presents an experimental comparative analysis of three decision-making approaches that have been utilized in interactive environments: namely, finite state machines, goal-oriented planning, and utility-based models. Each approach was implemented within a unified real-time simulation framework to ensure methodological consistency. The evaluation protocol assessed both behavioral effectiveness and computational performance, with particular emphasis on decision latency under increasing agent populations. Experimental results indicate that utility-based models produce the most adaptive and behaviorally optimal outcomes, albeit with a higher computational cost. Finite state machines demonstrate superior time efficiency and scalability, though at the expense of behavioral flexibility. Goal-oriented planning exhibits intermediate characteristics in terms of both adaptability and computational overhead. The findings provide a structured analysis of the trade-offs between behavioral optimality and computational efficiency, contributing to a deeper understanding of the scalability of decision-making architectures in real-time intelligent systems. |
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