125 citations · 485 across the 34 of their papers we have counts for
10 papers · 1 filter
The Invariant Ground Truth of Affect
Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis +1
Affective computing strives to unveil the unknown relationship between affect elicitation, manifestation of affect and affect annotations. The ground truth of affect, however, is p…
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…
Supervised Contrastive Learning for Affect Modelling
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis +1
Affect modeling is viewed, traditionally, as the process of mapping measurable affect manifestations from multiple modalities of user input to affect labels. That mapping is usuall…
Game State Learning via Game Scene Augmentation
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural conte…
Revisiting lp-constrained Softmax Loss: A Comprehensive Study
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large. The impact of particular forms of norma…
Learning Task-Independent Game State Representations from Unlabeled Images
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
Self-supervised learning (SSL) techniques have been widely used to learn compact and informative representations from high-dimensional complex data. In many computer vision tasks,…
Seeding Diversity into AI Art
Marvin Zammit, Antonios Liapis, Georgios N. Yannakakis
This paper argues that generative art driven by conformance to a visual and/or semantic corpus lacks the necessary criteria to be considered creative. Among several issues identifi…
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
Kosmas Pinitas, Konstantinos Makantasis, Antonios Liapis +1
Stochastic gradient descent (SGD) is a premium optimization method for training neural networks, especially for learning objectively defined labels such as image objects and events…