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Found 42 papers, 10 papers with code
Dynamic Quality-Diversity Search
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.
MAP-Elites with Transverse Assessment for Multimodal Problems in Creative Domains
Our approach represents a significant step forward in multimodal bottom-up orchestration and lays the groundwork for more complex systems coordinating multimodal creative agents in the future.
Large Language Models and Games: A Survey and Roadmap
Recent years have seen an explosive increase in research on large language models (LLMs), and accompanying public engagement on the topic.
Simulator-Free Visual Domain Randomization via Video Games
In a more challenging setting, BehAVE manages to improve the zero-shot transferability of foundation models to unseen FPS games (up to 22%) even when trained on a game of a different genre (Minecraft).
Affective Game Computing: A Survey
We review this emerging field, namely affective game computing, through the lens of the four core phases of the affective loop: game affect elicitation, game affect sensing, game affect detection and game affect adaptation.
Lode Enhancer: Level Co-creation Through Scaling
We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels.
Towards General Game Representations: Decomposing Games Pixels into Content and Style
On-screen game footage contains rich contextual information that players process when playing and experiencing a game.
From the Lab to the Wild: Affect Modeling via Privileged Information
Privileged information enables affect models to be trained across multiple modalities available in a lab, and ignore, without significant performance drops, those modalities that are not available when they operate in the wild.
The Ethics of AI in Games
Video games are one of the richest and most popular forms of human-computer interaction and, hence, their role is critical for our understanding of human behaviour and affect at a large scale.
Controllable Exploration of a Design Space via Interactive Quality Diversity
To address these concerns, we implement a variation of the MAP-Elites algorithm where the presented alternatives are sampled from a small region (window) of the behavioral space.
Choose Your Weapon: Survival Strategies for Depressed AI Academics
This is not an exhaustive list of strategies, and you may not agree with all of them, but it serves to start a discussion.
Architext: Language-Driven Generative Architecture Design
We conduct a thorough quantitative evaluation of Architext's downstream task performance, focusing on semantic accuracy and diversity for a number of pre-trained language models ranging from 120 million to 6 billion parameters.
The Invariant Ground Truth of Affect
In particular, we assume that the ground truth of affect can be found in the causal relationships between elicitation, manifestation and annotation that remain \emph{invariant} across tasks and participants.
Open-Ended Evolution for Minecraft Building Generation
To realize this goal we evaluate individuals' novelty in the latent space using a 3D autoencoder, and alternate between phases of exploration and transformation.
Play with Emotion: Affect-Driven Reinforcement Learning
According to the proposed paradigm, RL agents learn a policy (i. e. affective interaction) by attempting to maximize a set of rewards (i. e. behavioral and affective patterns) via their experience with their environment (i. e. context).
Generative Personas That Behave and Experience Like Humans
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 AI at large.
Supervised Contrastive Learning for Affect Modelling
Affect modeling is viewed, traditionally, as the process of mapping measurable affect manifestations from multiple modalities of user input to affect labels.
Game State Learning via Game Scene Augmentation
Having access to accurate game state information is of utmost importance for any artificial intelligence task including game-playing, testing, player modeling, and procedural content generation.
Revisiting lp-constrained Softmax Loss: A Comprehensive Study
Normalization is a vital process for any machine learning task as it controls the properties of data and affects model performance at large.
Learning Task-Independent Game State Representations from Unlabeled Images
We train an image encoder with three widely used SSL algorithms using solely the raw frames, and then attempt to recover the internal state variables from the learned representations.
Seeding Diversity into AI Art
Testing our hypothesis using novelty search with local competition, a quality-diversity evolutionary algorithm that can increase visual diversity while maintaining quality in the form of adherence to the semantic prompt, we explore how different notions of visual diversity can affect both the process and the product of the algorithm.
RankNEAT: Outperforming Stochastic Gradient Search in Preference Learning Tasks
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.
AffectGAN: Affect-Based Generative Art Driven by Semantics
This paper introduces a novel method for generating artistic images that express particular affective states.
Go-Blend behavior and affect
Our Go-Explore implementation not only introduces a new paradigm for affect modeling; it empowers believable AI-based game testing by providing agents that can blend and express a multitude of behavioral and affective patterns.
Keiki: Towards Realistic Danmaku Generation via Sequential GANs
Search-based procedural content generation methods have recently been introduced for the autonomous creation of bullet hell games.
Experience-Driven PCG via Reinforcement Learning: A Super Mario Bros Study
In particular, the RL designers of Super Mario Bros generate and concatenate level segments while considering the diversity among the segments.
Contrastive Learning of Generalized Game Representations
In this paper we build on recent advances in contrastive learning and showcase its benefits for representation learning in games.
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ARCH-Elites: Quality-Diversity for Urban Design
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 costly simulations.
Monte Carlo Elites: Quality-Diversity Selection as a Multi-Armed Bandit Problem
A core challenge of evolutionary search is the need to balance between exploration of the search space and exploitation of highly fit regions.
Pairing Character Classes in a Deathmatch Shooter Game via a Deep-Learning Surrogate Model
This paper introduces a surrogate model of gameplay that learns the mapping between different game facets, and applies it to a generative system which designs new content in one of these facets.
Transforming Exploratory Creativity with DeLeNoX
DeLeNoX proceeds in alternating phases of exploration and transformation.
The Pixels and Sounds of Emotion: General-Purpose Representations of Arousal in Games
What if emotion could be captured in a general and subject-agnostic fashion?
Human-Computer Interaction
Deep Learning for Procedural Content Generation
This article surveys the various deep learning methods that have been applied to generate game content directly or indirectly, discusses deep learning methods that could be used for content generation purposes but are rarely used today, and envisages some limitations and potential future directions of deep learning for procedural content generation.
Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch
Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry?
I Feel I Feel You: A Theory of Mind Experiment in Games
In this study into the player's emotional theory of mind of gameplaying agents, we investigate how an agent's behaviour and the player's own performance and emotions shape the recognition of a frustrated behaviour.
Procedural Content Generation through Quality Diversity
Quality-diversity (QD) algorithms search for a set of good solutions which cover a space as defined by behavior metrics.
From Pixels to Affect: A Study on Games and Player Experience
Is it possible to predict the affect of a user just by observing her behavioral interaction through a video?
Your Gameplay Says It All: Modelling Motivation in Tom Clancy's The Division
Is it possible to predict the motivation of players just by observing their gameplay data?
Quality Diversity Through Surprise
Quality diversity is a recent family of evolutionary search algorithms which focus on finding several well-performing (quality) yet different (diversity) solutions with the aim to maintain an appropriate balance between divergence and convergence during search.
Surprise Search for Evolutionary Divergence
Inspired by the notion of surprise for unconventional discovery we introduce a general search algorithm we name surprise search as a new method of evolutionary divergent search.
The Preference Learning Toolbox
Preference learning (PL) is a core area of machine learning that handles datasets with ordinal relations.
Active Player Modelling
We further hypothesise that this form of curiosity is symmetric, and therefore that games that explore their players based on the principles of active learning will turn out to select game configurations that are interesting to the player that is being explored.
