System provides quick obstacle solution feedback for 3D game design
Providing Rapid Design Feedback for 3D Obstacle Course Games Using Constrained Solvability Queries
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Summary
Designing challenging 3D obstacle courses can be slow because designers need to know if and how players can complete obstacles. The authors created a system that quickly finds ways to solve these obstacles while following specific rules set by the designer, like only jumping twice or avoiding certain areas. Their system uses a fast game simulation and smart search methods to test many possible solutions very quickly. Designers can use this tool to understand all the possible ways an obstacle can be solved or why it can’t be, helping them make better games that play the way they want.
What this means in practice
- •For game designers: Rapidly test and understand how players can solve custom 3D obstacle course challenges under various constraints during game development.
- •For game developers: Efficiently generate and assess procedural obstacle courses with predictable player solutions and difficulty evaluations.
Authors
Zander Majercik, Sharon Zhang, William Wang, Tejan Karmali, Fangjun Zhou, Yucheng Yuan, Jean-Peïc Chou, Maneesh Agrawala, Kayvon Fatahalian
Abstract
We present a system that aids the design of 3D obstacle course games by providing designers with rapid feedback on how obstacles can be solved. Our core contribution is a system for querying for solutions (sequences of player actions) to an obstacle that adhere to designer-specified constraints (e.g., avoid a region, travel through a given waypoint, only perform two jumps, etc.). To solve a wide range of obstacle designs quickly, we author a high-performance implementation of the GoExplore algorithm for exploratory search, and guide search with an obstacle solving agent trained offline using reinforcement learning (RL). To further accelerate search, the system carries out exploration using a custom GPU-accelerated obstacle course game simulator that generates playthrough experience at nearly 14,000$\times$ real time, 60-fps gameplay. Through design studies, we demonstrate that the use of constrained solvability queries in a rapid design loop is sufficiently expressive to help designers understand ways an obstacle can be solved or why it cannot be solved. We also show how the tool can let designers answer higher-level questions such as identifying undesirable solution paths and assessing the difficulty of solutions. This allows them to pursue new design directions they did not originally anticipate. Human playtesting of obstacles designed using our system confirms that human players indeed play the obstacles in the manner the designers intended. We release code for our interactive tool, simulator, training setup, and procedural level generation system at https://zandermajercik.github.io/interactive-obstacle-course-feedback/.