159 citations · 342 across the 21 of their papers we have counts for
21 papers
Reinforcement learning
Sarod Yatawatta
Observing celestial objects and advancing our scientific knowledge about them involves tedious planning, scheduling, data collection and data post-processing. Many of these operati…
Modeling Cognitive-Affective Processes with Appraisal and Reinforcement Learning
Jiayi Zhang, Joost Broekens, Jussi Jokinen
Computational models can advance affective science by shedding light onto the interplay between cognition and emotion from an information processing point of view. We propose a com…
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Joost Broekens, Bernhard Hilpert, Suzan Verberne +3
Large language models, in particular generative pre-trained transformers (GPTs), show impressive results on a wide variety of language-related tasks. In this paper, we explore Chat…
A Valid Self-Report is Never Late, Nor is it Early: On Considering the "Right" Temporal Distance for Assessing Emotional Experience
Bernd Dudzik, Joost Broekens
Developing computational models for automatic affect prediction requires valid self-reports about individuals' emotional interpretations of stimuli. In this article, we highlight t…
A Blast From the Past: Personalizing Predictions of Video-Induced Emotions using Personal Memories as Context
Bernd Dudzik, Joost Broekens, Mark Neerincx +1
A key challenge in the accurate prediction of viewers' emotional responses to video stimuli in real-world applications is accounting for person- and situation-specific variation. A…
Model-based Reinforcement Learning: A Survey
Thomas M. Moerland, Joost Broekens, Aske Plaat +1
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelligence. Two key approaches to this probl…