Search Results for author:
Found 44 papers, 7 papers with code
Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning
In this work, we show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents in a complex simulation environment.
Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach
We consider TRP to be a promising new approach that can afford the introduction of PCGML into the early stages of game development without requiring human expertise or significant training data.
Mechanic Maker 2.0: Reinforcement Learning for Evaluating Generated Rules
Automated game design (AGD), the study of automatically generating game rules, has a long history in technical games research.
Reconstructing Existing Levels through Level Inpainting
Procedural Content Generation (PCG) and Procedural Content Generation via Machine Learning (PCGML) have been used in prior work for generating levels in various games.
Joint Level Generation and Translation Using Gameplay Videos
In this work, we aim to address this problem by utilizing gameplay videos of two human-annotated games to develop a novel multi-tail framework that learns to perform simultaneous level translation and generation.
Game Level Blending using a Learned Level Representation
Game level blending via machine learning, the process of combining features of game levels to create unique and novel game levels using Procedural Content Generation via Machine Learning (PCGML) techniques, has gained increasing popularity in recent years.
Transfer Learning for Underrepresented Music Generation
This paper investigates a combinational creativity approach to transfer learning to improve the performance of deep neural network-based models for music generation on out-of-distribution (OOD) genres.
Procedural Content Generation via Knowledge Transformation (PCG-KT)
We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT), a new lens and framework for characterizing PCG methods and approaches in which content generation is enabled by the process of knowledge transformation -- transforming knowledge derived from one domain in order to apply it in another.
Improving Deep Localized Level Analysis: How Game Logs Can Help
Player modelling is the field of study associated with understanding players.
Generating Real-Time Strategy Game Units Using Search-Based Procedural Content Generation and Monte Carlo Tree Search
Real-Time Strategy (RTS) game unit generation is an unexplored area of Procedural Content Generation (PCG) research, which leaves the question of how to automatically generate interesting and balanced units unanswered.
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
In fighting games, individual players of the same skill level often exhibit distinct strategies from one another through their gameplay.
Clustering-based Tile Embedding (CTE): A General Representation for Level Design with Skewed Tile Distributions
There has been significant research interest in Procedural Level Generation via Machine Learning (PLGML), applying ML techniques to automated level generation.
Responsibility: An Example-based Explainable AI approach via Training Process Inspection
Explainable Artificial Intelligence (XAI) methods are intended to help human users better understand the decision making of an AI agent.
SketchBetween: Video-to-Video Synthesis for Sprite Animation via Sketches
We propose a problem formulation that more closely adheres to the standard workflow of animation.
Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems
To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them.
Pixel VQ-VAEs for Improved Pixel Art Representation
Machine learning has had a great deal of success in image processing.
Tile Embedding: A General Representation for Procedural Level Generation via Machine Learning
We evaluate this representation on its ability to predict affordances for unseen tiles, and to serve as a PLGML representation for annotated and unannotated games.
Explaining Deep Reinforcement Learning Agents In The Atari Domain through a Surrogate Model
One major barrier to applications of deep Reinforcement Learning (RL) both inside and outside of games is the lack of explainability.
Conceptual Expansion Neural Architecture Search (CENAS)
Architecture search optimizes the structure of a neural network for some task instead of relying on manual authoring.
The Impact of Visualizing Design Gradients for Human Designers
Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content.
Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)
Personalized therapy, in which a therapeutic practice is adapted to an individual patient, leads to better health outcomes.
Ensemble Learning For Mega Man Level Generation
Procedural content generation via machine learning (PCGML) is the process of procedurally generating game content using models trained on existing game content.
Toward Co-creative Dungeon Generation via Transfer Learning
Co-creative Procedural Content Generation via Machine Learning (PCGML) refers to systems where a PCGML agent and a human work together to produce output content.
Generating Lode Runner Levels by Learning Player Paths with LSTMs
Machine learning has been a popular tool in many different fields, including procedural content generation.
TaikoNation: Patterning-focused Chart Generation for Rhythm Action Games
Generating rhythm game charts from songs via machine learning has been a problem of increasing interest in recent years.
Adversarial Random Forest Classifier for Automated Game Design
Autonomous game design, generating games algorithmically, has been a longtime goal within the technical games research field.
Generating Gameplay-Relevant Art Assets with Transfer Learning
In game development, designing compelling visual assets that convey gameplay-relevant features requires time and experience.
Entity Embedding as Game Representation
However, the majority of the work has focused on the production of static game content, including game levels and visual elements.
Explainability via Responsibility
We evaluate this approach by approximating its ability to provide human users with the explanations of AI agent's actions and helping them to more efficiently cooperate with the AI agent.
BIG-bench Machine Learning
Explainable artificial intelligence
Tabletop Roleplaying Games as Procedural Content Generators
Tabletop roleplaying games (TTRPGs) and procedural content generators can both be understood as systems of rules for producing content.
Conceptual Game Expansion
Automated game design is the problem of automatically producing games through computational processes.
Integrating Automated Play in Level Co-Creation
In level co-creation an AI and human work together to create a video game level.
Automated Let's Play Commentary
Let's Plays of video games represent a relatively unexplored area for experimental AI in games.
Making CNNs for Video Parsing Accessible
This serves as a barrier to groups who don't possess this access.
An Interaction Framework for Studying Co-Creative AI
Machine learning has been applied to a number of creative, design-oriented tasks.
Explainable PCGML via Game Design Patterns
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning.
Co-Creative Level Design via Machine Learning
Procedural Level Generation via Machine Learning (PLGML), the study of generating game levels with machine learning, has received a large amount of recent academic attention.
Towards Automated Let's Play Commentary
We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning.
Automated Game Design via Conceptual Expansion
To the best of our knowledge, this represents the first machine learning-based automated game design system.
Creative Invention Benchmark
In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity.
Combinets: Creativity via Recombination of Neural Networks
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge.
Procedural Content Generation via Machine Learning (PCGML)
This survey explores Procedural Content Generation via Machine Learning (PCGML), defined as the generation of game content using machine learning models trained on existing content.
Learning to Blend Computer Game Levels
We evaluate the extent to which the models represent stylistic level design knowledge and demonstrate the ability of our system to explain levels that were blended by human expert designers.
Toward Game Level Generation from Gameplay Videos
We further demonstrate how the acquired design knowledge can be used to generate sections of game levels.
