The recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and... Show moreThe recent surge in deployment and use of generative machine learning models has sparked an interest in the relationships between AI and creativity, or more specifically into the question and debate of whether machines can exhibit human-level creativity. This is by no means a new discussion, going back in time decades if not centuries. The debate was approached from multiple angles, and a general consensus was not yet reached. In this position paper, we present the long-standing debate as it formed across various fields such as cognitive science, philosophy, and computing, approaching it mainly from a historical perspective. Along the way we identify how the various views relate to recent developments in machine learning models and argue our own position regarding the question of whether machines can exhibit human-level creativity. As such we aim to involve computer scientists and AI practitioners into the ongoing debate. Show less
Stein. N. van; Winter, R. de; Bäck, T.H.W.; Kononova, A. V. 2023
The ability to extract features from objects or concepts and to connect them in a meaningful way is believed to be crucial to creativity. This paper proposes a novel computational model for... Show moreThe ability to extract features from objects or concepts and to connect them in a meaningful way is believed to be crucial to creativity. This paper proposes a novel computational model for creative behaviour, by learning extractions and connections separately. Such separation enables adaptive feature and object connections, which means one object can be connected to different other objects within different contexts. This paper applies recent cognitive theories of computational creativity to two specific tasks: an image-to-image mapping and musicto- video mapping. In both cases deep neural networks are used to automate these two creative cognition tasks. Show less
In this thesis we have studied the influence of emotion on learning. We have used computational modelling techniques to do so, more specifically, the reinforcement learning paradigm. Emotion is... Show moreIn this thesis we have studied the influence of emotion on learning. We have used computational modelling techniques to do so, more specifically, the reinforcement learning paradigm. Emotion is modelled as artificial affect, a measure that denotes the positiveness versus negativeness of a situation to an artificial agent in a reinforcement learning setting. We have done a range of different experiments to study the effect of affect on learning, including the effect on learning if affect is used to control the exploration behaviour of the agent and the effect on learning when affect is communicated by a human (though real-time analysis of that human__s facial expressions) to a simulated robot. We conclude that affect is a useful concept to consider in adaptive agents that learn based on reinforcement learning and that in some cases affect can indeed help the learning process. Further, affective modelling in this way can help understand the psychological processes that underlie influences of affect on cognition. Finally, we have developed a formal notation for a specific type of emotion theory, i.e., cognitive appraisal theory. Show less
