3 papers
cs.AI2024
Affectively Framework: Towards Human-like Affect-Based Agents
Matthew Barthet, Roberto Gallotta, Ahmed Khalifa +2
Game environments offer a unique opportunity for training virtual agents due to their interactive nature, which provides diverse play traces and affect labels. Despite their potent…
cs.HC2024
Closing the Affective Loop via Experience-Driven Reinforcement Learning Designers
Matthew Barthet, Diogo Branco, Roberto Gallotta +2
Autonomously tailoring content to a set of predetermined affective patterns has long been considered the holy grail of affect-aware human-computer interaction at large. The experie…
cs.NE2024
Dynamic Quality-Diversity Search
Roberto Gallotta, Antonios Liapis, Georgios N. Yannakakis
Evolutionary search via the quality-diversity (QD) paradigm can discover highly performing solutions in different behavioural niches, showing considerable potential in complex real…