Summary of Evaluating Environments Using Exploratory Agents, by Bobby Khaleque et al.
Evaluating Environments Using Exploratory Agents
by Bobby Khaleque, Mike Cook, Jeremy Gow
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the use of an exploratory agent to provide feedback on the design of procedurally generated game levels. The researchers investigate the motivations behind exploration in games, developing a framework that models these motivations. They also introduce a fitness function for evaluating an environment’s potential for exploration. The study finds that their agent can effectively distinguish between engaging and unengaging levels, suggesting its potential as a tool for assessing procedurally generated levels. This work contributes to AI-driven game design by offering new insights into how game environments can be evaluated and optimized for player exploration. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Exploration is important in many video games. Scientists created an agent that helps designers make good game levels that players will enjoy exploring. They tested the agent with 10 levels, five that were fun to explore and five that weren’t. The results show that their agent can tell which levels are engaging or not. This research can help make better game designs by understanding what makes levels interesting for players. |
