most citedCreativity and Markov Decision Processes

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI2025

Towards a Formal Theory of the Need for Competence via Computational Intrinsic Motivation

Erik M. Lintunen, Nadia M. Ady, Sebastian Deterding +1

Computational modelling offers a powerful tool for formalising psychological theories, making them more transparent, testable, and applicable in digital contexts. Yet, the question…

cs.NE2025

Untapped Potential in Self-Optimization of Hopfield Networks: The Creativity of Unsupervised Learning

Natalya Weber, Christian Guckelsberger, Tom Froese

The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network, leveraging the power of associative memory to enhance optimizati…

cs.AI2024

Diversity Progress for Goal Selection in Discriminability-Motivated RL

Erik M. Lintunen, Nadia M. Ady, Christian Guckelsberger

Non-uniform goal selection has the potential to improve the reinforcement learning (RL) of skills over uniform-random selection. In this paper, we introduce a method for learning a…

cs.AI2024

On Creativity and Open-Endedness

L. B. Soros, Alyssa Adams, Stefano Kalonaris +2

Artificial Life (ALife) as an interdisciplinary field draws inspiration and influence from a variety of perspectives. Scientific progress crucially depends, then, on concerted effo…

cs.AI2024★ 1 cited

Creativity and Markov Decision Processes

Joonas Lahikainen, Nadia M. Ady, Christian Guckelsberger

Creativity is already regularly attributed to AI systems outside specialised computational creativity (CC) communities. However, the evaluation of creativity in AI at large typical…