The Many Challenges of Human-Like Agents in Virtual Game Environments

• URL: http://arxiv.org/abs/2505.20011v3
• Date: Mon, 09 Jun 2025 20:57:51 GMT
• Title: The Many Challenges of Human-Like Agents in Virtual Game Environments
• Authors: Maciej Swiechowski, Dominik Slezak,
• Abstract summary: This article is a survey of the most significant challenges in implementing human-like AI in games.<n>A machine-learning approach using a custom deep recurrent convolutional neural network is presented.<n>We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.
• Score: 1.1586742546971471
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-like agents are an increasingly important topic in games and beyond. Believable non-player characters enhance the gaming experience by improving immersion and providing entertainment. They also offer players the opportunity to engage with AI entities that can function as opponents, teachers, or cooperating partners. Additionally, in games where bots are prohibited -- and even more so in non-game environments -- there is a need for methods capable of identifying whether digital interactions occur with bots or humans. This leads to two fundamental research questions: (1) how to model and implement human-like AI, and (2) how to measure its degree of human likeness. This article offers two contributions. The first one is a survey of the most significant challenges in implementing human-like AI in games (or any virtual environment featuring simulated agents, although this article specifically focuses on games). Thirteen such challenges, both conceptual and technical, are discussed in detail. The second is an empirical study performed in a tactical video game that addresses the research question: "Is it possible to distinguish human players from bots (AI agents) based on empirical data?" A machine-learning approach using a custom deep recurrent convolutional neural network is presented. We hypothesize that the more challenging it is to create human-like AI for a given game, the easier it becomes to develop a method for distinguishing humans from AI-driven players.

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