Investigating Human Priors for Playing Video Games (1802.10217v3)

Abstract: What makes humans so good at solving seemingly complex video games? Unlike computers, humans bring in a great deal of prior knowledge about the world, enabling efficient decision making. This paper investigates the role of human priors for solving video games. Given a sample game, we conduct a series of ablation studies to quantify the importance of various priors on human performance. We do this by modifying the video game environment to systematically mask different types of visual information that could be used by humans as priors. We find that removal of some prior knowledge causes a drastic degradation in the speed with which human players solve the game, e.g. from 2 minutes to over 20 minutes. Furthermore, our results indicate that general priors, such as the importance of objects and visual consistency, are critical for efficient game-play. Videos and the game manipulations are available at https://rach0012.github.io/humanRL_website/

Investigating Human Priors for Playing Video Games

The paper "Investigating Human Priors for Playing Video Games," authored by Rachit Dubey et al., explores the cognitive capabilities that humans leverage to excel in video games, contrasting these with computational methods. Unlike computers, which typically rely on brute-force algorithms and extensive data processing, humans inherently utilize prior knowledge derived from everyday experiences. This research explores the impact and significance of such human priors in the context of video game problem-solving.

In their methodology, the authors conducted a series of controlled experiments where they systematically altered the gaming environment to obscure various types of visual information. These modifications aimed to identify which specific priors most significantly affect human performance. The hypothesis was that certain prior knowledge is indispensable for maintaining efficiency during gameplay. The research demonstrated that the absence of crucial prior information could prolong the time required by human participants to solve games—from instances of approximately 2 minutes extending to over 20 minutes—illustrating the profound effect of these priors.

The findings underscore the critical role general priors play. Specifically, the importance of objects and visual consistency were identified as paramount for efficient game navigation and decision-making. These insights suggest that humans rely significantly on both object recognition and the assumption of continuity in visual environments to interact effectively with video games.

The implications of this work are broad, extending beyond mere gaming contexts into the fields of artificial intelligence, cognitive science, and human-computer interaction. For AI development, understanding human priors could advance the creation of models that better mimic human-like reasoning and decision-making by integrating similar knowledge-based shortcuts. From a cognitive science perspective, these findings contribute to a deeper understanding of how humans deploy environmental knowledge to solve complex tasks rapidly. In practical terms, this could inspire the design of more intuitive user interfaces and educational tools that align better with natural human cognitive processes.

Looking forward, the authors' work provokes several questions and prospective avenues for future research. One inquiry pertains to the extensibility of these findings across different genres of games and dynamic environments. Additionally, integrating these insights into reinforcement learning frameworks presents an enticing opportunity to enhance machine learning models by embedding human-like priors, thereby reducing the reliance on extensive training data and improving generalization in novel scenarios. The interdisciplinary nature of this research bridges cognitive psychology and artificial intelligence, offering a promising template for developing more advanced cognitive models and AI systems.