Summary of Creativity and Markov Decision Processes, by Joonas Lahikainen et al.
Creativity and Markov Decision Processes
by Joonas Lahikainen, Nadia M. Ady, Christian Guckelsberger
First submitted to arxiv on: 23 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 The paper bridges the gap between artificial intelligence (AI) research and specialized computational creativity (CC) communities by formalizing a mapping between Boden’s process theory of creativity and Markov Decision Processes (MDPs). This effort aims to promote more meaningful evaluations of creativity in AI systems, currently limited by a lack of grounding in creativity theory. The authors identify three out of eleven potential mappings between the two frameworks, analyzing their implications for creative processes, opportunities, and threats. They also outline quality criteria for selecting such mappings for future applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers are trying to make artificial intelligence systems more creative. Right now, we don’t really know how to measure creativity in AI because we’re not using the right theories from psychology. The authors of this paper found a way to connect two important ideas: Boden’s theory of creativity and something called Markov Decision Processes. They looked at three specific ways that these ideas could work together and what they mean for making AI systems more creative. The goal is to help us make better AI systems that can think creatively. |
Keywords
» Artificial intelligence » Grounding
