125 citations · 485 across the 34 of their papers we have counts for
45 papers
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…
Towards General Game Representations: Decomposing Games Pixels into Content and Style
Chintan Trivedi, Konstantinos Makantasis, Antonios Liapis +1
On-screen game footage contains rich contextual information that players process when playing and experiencing a game. Learning pixel representations of games can benefit artificia…
From the Lab to the Wild: Affect Modeling via Privileged Information
Konstantinos Makantasis, Kosmas Pinitas, Antonios Liapis +1
How can we reliably transfer affect models trained in controlled laboratory conditions (in-vitro) to uncontrolled real-world settings (in-vivo)? The information gap between in-vitr…
Controllable Exploration of a Design Space via Interactive Quality Diversity
Konstantinos Sfikas, Antonios Liapis, Georgios N. Yannakakis
This paper introduces a user-driven evolutionary algorithm based on Quality Diversity (QD) search. During a design session, the user iteratively selects among presented alternative…
Architext: Language-Driven Generative Architecture Design
Theodoros Galanos, Antonios Liapis, Georgios N. Yannakakis
Architectural design is a highly complex practice that involves a wide diversity of disciplines, technologies, proprietary design software, expertise, and an almost infinite number…
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…
"What Artists Want": Elicitation of Artist Requirements to Feed the Design on a New Collaboration Platform for Creative Work
Angeliki Antoniou, Ioanna Lykourentzou, Antonios Liapis +2
Aiming at designing a decentralized platform to support grassroot initiatives for self-organized creative work, the present work solicited feedback from a group of visual artists r…
Towards General Models of Player Experience: A Study Within Genres
David Melhart, Antonios Liapis, Georgios N. Yannakakis
To which degree can abstract gameplay metrics capture the player experience in a general fashion within a game genre? In this comprehensive study we address this question across th…
AffectGAN: Affect-Based Generative Art Driven by Semantics
Theodoros Galanos, Antonios Liapis, Georgios N. Yannakakis
This paper introduces a novel method for generating artistic images that express particular affective states. Leveraging state-of-the-art deep learning methods for visual generatio…
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…
Privileged Information for Modeling Affect In The Wild
Konstantinos Makantasis, David Melhart, Antonios Liapis +1
A key challenge of affective computing research is discovering ways to reliably transfer affect models that are built in the laboratory to real world settings, namely in the wild.…
Contrastive Learning of Generalized Game Representations
Chintan Trivedi, Antonios Liapis, Georgios N. Yannakakis
Representing games through their pixels offers a promising approach for building general-purpose and versatile game models. While games are not merely images, neural network models…
Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
Konstantinos Sfikas, Antonios Liapis, Georgios N. Yannakakis
A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions. Quality-diversity search has explicit…
ARCH-Elites: Quality-Diversity for Urban Design
Theodoros Galanos, Antonios Liapis, Georgios N. Yannakakis +1
This paper introduces ARCH-Elites, a MAP-Elites implementation that can reconfigure large-scale urban layouts at real-world locations via a pre-trained surrogate model instead of c…
The Arousal video Game AnnotatIoN (AGAIN) Dataset
David Melhart, Antonios Liapis, Georgios N. Yannakakis
How can we model affect in a general fashion, across dissimilar tasks, and to which degree are such general representations of affect even possible? To address such questions and e…