122 citations · 122 across the 6 of their papers we have counts for
6 papers
The Procedural Content Generation Benchmark: An Open-source Testbed for Generative Challenges in Games
Ahmed Khalifa, Roberto Gallotta, Matthew Barthet +3
This paper introduces the Procedural Content Generation Benchmark for evaluating generative algorithms on different game content creation tasks. The benchmark comes with 12 game-re…
FREYR: A Framework for Recognizing and Executing Your Requests
Roberto Gallotta, Antonios Liapis, Georgios N. Yannakakis
Large language models excel as conversational agents, but their capabilities can be further extended through tool usage, i.e.: executable code, to enhance response accuracy or addr…
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…
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…
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…
Large Language Models and Games: A Survey and Roadmap
Roberto Gallotta, Graham Todd, Marvin Zammit +4
Recent years have seen an explosive increase in research on large language models (LLMs), and accompanying public engagement on the topic. While starting as a niche area within nat…