The Many Challenges of Human-Like Agents in Virtual Game Environments
Maciej Swiechowski, Dominik Slezak

TL;DR
This paper reviews key challenges in creating human-like AI in virtual environments and empirically tests whether humans can be distinguished from AI in a tactical game using deep learning.
Contribution
It provides a comprehensive survey of 13 challenges in human-like AI development and presents an empirical study on differentiating humans from AI using deep neural networks.
Findings
Identified 13 major challenges in implementing human-like AI.
Demonstrated that deep learning can distinguish humans from AI in gameplay.
Suggested that difficulty in creating human-like AI correlates with ease of detection.
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…
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Taxonomy
TopicsRobotic Path Planning Algorithms
