25 citations · 70 across the 11 of their papers we have counts for
12 papers
Emotions as Ambiguity-aware Ordinal Representations
Jingyao Wu, Matthew Barthet, David Melhart +1
Emotions are inherently ambiguous and dynamic phenomena, yet existing continuous emotion recognition approaches either ignore their ambiguity or treat ambiguity as an independent a…
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…
Can Large Language Models Capture Video Game Engagement?
David Melhart, Matthew Barthet, Georgios N. Yannakakis
Can out-of-the-box pretrained Large Language Models (LLMs) detect human affect successfully when observing a video? To address this question, for the first time, we evaluate compre…
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…
GameVibe: A Multimodal Affective Game Corpus
Matthew Barthet, Maria Kaselimi, Kosmas Pinitas +3
As online video and streaming platforms continue to grow, affective computing research has undergone a shift towards more complex studies involving multiple modalities. However, th…
Predicting Player Engagement in Tom Clancy's The Division 2: A Multimodal Approach via Pixels and Gamepad Actions
Kosmas Pinitas, David Renaudie, Mike Thomsen +4
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…
Knowing Your Annotator: Rapidly Testing the Reliability of Affect Annotation
Matthew Barthet, Chintan Trivedi, Kosmas Pinitas +4
The laborious and costly nature of affect annotation is a key detrimental factor for obtaining large scale corpora with valid and reliable affect labels. Motivated by the lack of t…
Open-Ended Evolution for Minecraft Building Generation
Matthew Barthet, Antonios Liapis, Georgios N. Yannakakis
This paper proposes a procedural content generator which evolves Minecraft buildings according to an open-ended and intrinsic definition of novelty. To realize this goal we evaluat…
Generative Personas That Behave and Experience Like Humans
Matthew Barthet, Ahmed Khalifa, Antonios Liapis +1
Using artificial intelligence (AI) to automatically test a game remains a critical challenge for the development of richer and more complex game worlds and for the advancement of A…
Play with Emotion: Affect-Driven Reinforcement Learning
Matthew Barthet, Ahmed Khalifa, Antonios Liapis +1
This paper introduces a paradigm shift by viewing the task of affect modeling as a reinforcement learning (RL) process. According to the proposed paradigm, RL agents learn a policy…
Go-Blend behavior and affect
Matthew Barthet, Antonios Liapis, Georgios N. Yannakakis
This paper proposes a paradigm shift for affective computing by viewing the affect modeling task as a reinforcement learning process. According to our proposed framework the contex…