Creativity and Markov Decision Processes
This work addresses the gap between creativity theory and common AI frameworks for researchers in computational creativity, but it is incremental as it focuses on formal mappings without new empirical results.
The paper tackled the problem of evaluating creativity in AI by mapping Boden's process theory of creativity to Markov Decision Processes (MDPs), identifying three out of eleven mappings to analyze creative processes, opportunities, and threats.
Creativity is already regularly attributed to AI systems outside specialised computational creativity (CC) communities. However, the evaluation of creativity in AI at large typically lacks grounding in creativity theory, which can promote inappropriate attributions and limit the analysis of creative behaviour. While CC researchers have translated psychological theory into formal models, the value of these models is limited by a gap to common AI frameworks. To mitigate this limitation, we identify formal mappings between Boden's process theory of creativity and Markov Decision Processes (MDPs), using the Creative Systems Framework as a stepping stone. We study three out of eleven mappings in detail to understand which types of creative processes, opportunities for (aberrations), and threats to creativity (uninspiration) could be observed in an MDP. We conclude by discussing quality criteria for the selection of such mappings for future work and applications.