Evaluating Environments Using Exploratory Agents

TL;DR

This paper presents an exploratory agent-based framework to evaluate the engagement potential of procedurally generated game levels, demonstrating its effectiveness in distinguishing engaging environments and aiding game design.

Contribution

It introduces a fitness function for environment evaluation and expands existing models of exploration motivation, advancing AI tools for game level assessment.

Findings

Agent distinguishes engaging from unengaging levels

Framework effectively assesses exploration potential

Supports AI-driven game environment optimization

Abstract

Exploration is a key part of many video games. We investigate the using an exploratory agent to provide feedback on the design of procedurally generated game levels, 5 engaging levels and 5 unengaging levels. We expand upon a framework introduced in previous research which models motivations for exploration and introduce a fitness function for evaluating an environment's potential for exploration. Our study showed that our exploratory agent can clearly distinguish between engaging and unengaging levels. The findings suggest that our agent has the potential to serve as an effective tool for assessing procedurally generated levels, in terms of exploration. This work contributes to the growing field of AI-driven game design by offering new insights into how game environments can be evaluated and optimised for player exploration.