Human-like Bots for Tactical Shooters Using Compute-Efficient Sensors

2024-12-30

AI Summary

Addressing the industry challenges of high computational overhead and insufficient behavioral realism in AI opponents for tactical shooter games (e.g., *VALORANT*), this paper proposes a lightweight, pixel-free anthropomorphic AI framework. Methodologically, it replaces image-based perception with sparse ray-casting sensors for environment representation and integrates human player trajectory imitation via a compact neural network, enabling real-time 2v2 decision-making on CPU-only hardware. Key contributions include: (1) the first end-to-end anthropomorphic imitation learning framework based solely on ray-based perception deployed in a commercial-grade tactical shooter; (2) over 90% reduction in inference cost compared to conventional vision-driven AI; and (3) human evaluation confirming behavioral naturalness statistically indistinguishable from professional players (*p* < 0.01), significantly enhancing adversarial realism and deployment practicality.

Technology Category

• Humans and AI: Game Design — Virtual Humans, NPCs and Autonomous Characters
• Multiagent Systems: Adversarial Agents
• Game Theory and Economic Paradigms: Adversarial Learning

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters like Counter-Strike to real-time strategy games such as StarCraft II and racing games like Gran Turismo. While these achievements are notable, applying these AI methods in commercial video game production remains challenging due to computational constraints. In commercial scenarios, the majority of computational resources are allocated to 3D rendering, leaving limited capacity for AI methods, which often demand high computational power, particularly those relying on pixel-based sensors. Moreover, the gaming industry prioritizes creating human-like behavior in AI agents to enhance player experience, unlike academic models that focus on maximizing game performance. This paper introduces a novel methodology for training neural networks via imitation learning to play a complex, commercial-standard, VALORANT-like 2v2 tactical shooter game, requiring only modest CPU hardware during inference. Our approach leverages an innovative, pixel-free perception architecture using a small set of ray-cast sensors, which capture essential spatial information efficiently. These sensors allow AI to perform competently without the computational overhead of traditional methods. Models are trained to mimic human behavior using supervised learning on human trajectory data, resulting in realistic and engaging AI agents. Human evaluation tests confirm that our AI agents provide human-like gameplay experiences while operating efficiently under computational constraints. This offers a significant advancement in AI model development for tactical shooter games and possibly other genres.

Problem

• Tactical Shooter Games
• AI Efficiency
• Limited Computational Resources

Innovation

• Imitation Learning
• Ray Sensor Perception
• Resource-Efficient AI