Evaluating Environments Using Exploratory Agents
This work provides a tool for game designers to assess and optimize levels for player exploration, though it is incremental as it builds on prior frameworks.
The paper tackled the problem of evaluating procedurally generated game levels for exploration by using an exploratory agent with a new fitness function, showing it could clearly distinguish between engaging and unengaging levels.
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.