Zobrazeno 1 - 10
of 467
pro vyhledávání: '"Yannakakis, Georgios N."'
Domain generalisation involves learning artificial intelligence (AI) models that can maintain high performance across diverse domains within a specific task. In video games, for instance, such AI models can supposedly learn to detect player actions a
Externí odkaz:
http://arxiv.org/abs/2409.13002
Autor:
Barthet, Matthew, Gallotta, Roberto, Khalifa, Ahmed, Liapis, Antonios, Yannakakis, Georgios N.
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 potential, no reinforcement learning framework incorporates human affect mod
Externí odkaz:
http://arxiv.org/abs/2407.18316
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 experience-driven procedural content generation framework realises this visio
Externí odkaz:
http://arxiv.org/abs/2408.06346
Autor:
Barthet, Matthew, Kaselimi, Maria, Pinitas, Kosmas, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, there is still a lack of readily available datasets with high-quality au
Externí odkaz:
http://arxiv.org/abs/2407.12787
Evolutionary search via the quality-diversity (QD) paradigm can discover highly performing solutions in different behavioural niches, showing considerable potential in complex real-world scenarios such as evolutionary robotics. Yet most QD methods on
Externí odkaz:
http://arxiv.org/abs/2404.05769
The recent advances in language-based generative models have paved the way for the orchestration of multiple generators of different artefact types (text, image, audio, etc.) into one system. Presently, many open-source pre-trained models combine tex
Externí odkaz:
http://arxiv.org/abs/2403.07182
Autor:
Gallotta, Roberto, Todd, Graham, Zammit, Marvin, Earle, Sam, Liapis, Antonios, Togelius, Julian, Yannakakis, Georgios N.
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 natural language processing, LLMs have shown remarkable potential across
Externí odkaz:
http://arxiv.org/abs/2402.18659
Autor:
Rašajski, Nemanja, Trivedi, Chintan, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
Domain randomisation enhances the transferability of vision models across visually distinct domains with similar content. However, current methods heavily depend on intricate simulation engines, hampering feasibility and scalability. This paper intro
Externí odkaz:
http://arxiv.org/abs/2402.01335
Autor:
Pinitas, Kosmas, Renaudie, David, Thomsen, Mike, Barthet, Matthew, Makantasis, Konstantinos, Liapis, Antonios, Yannakakis, Georgios N.
This paper introduces a large scale multimodal corpus collected for the purpose of analysing and predicting player engagement in commercial-standard games. The corpus is solicited from 25 players of the action role-playing game Tom Clancy's The Divis
Externí odkaz:
http://arxiv.org/abs/2310.06136
Publikováno v:
Proceedings of the IEEE, 2023
This paper surveys the current state of the art in affective computing principles, methods and tools as applied to games. We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop:
Externí odkaz:
http://arxiv.org/abs/2309.14104