93 citations · 111 across the 11 of their papers we have counts for
6 papers · 1 filter
Evolutionary Machine Learning and Games
Julian Togelius, Ahmed Khalifa, Sam Earle +2
Evolutionary machine learning (EML) has been applied to games in multiple ways, and for multiple different purposes. Importantly, AI research in games is not only about playing gam…
Preference-Learning Emitters for Mixed-Initiative Quality-Diversity Algorithms
Roberto Gallotta, Kai Arulkumaran, L. B. Soros
In mixed-initiative co-creation tasks, wherein a human and a machine jointly create items, it is important to provide multiple relevant suggestions to the designer. Quality-diversi…
Surrogate Infeasible Fitness Acquirement FI-2Pop for Procedural Content Generation
Roberto Gallotta, Kai Arulkumaran, L. B. Soros
When generating content for video games using procedural content generation (PCG), the goal is to create functional assets of high quality. Prior work has commonly leveraged the fe…
Selecting for Selection: Learning To Balance Adaptive and Diversifying Pressures in Evolutionary Search
Kevin Frans, L. B. Soros, Olaf Witkowski
Inspired by natural evolution, evolutionary search algorithms have proven remarkably capable due to their dual abilities to radiantly explore through diverse populations and to con…
Co-generation of game levels and game-playing agents
Aaron Dharna, Julian Togelius, L. B. Soros
Open-endedness, primarily studied in the context of artificial life, is the ability of systems to generate potentially unbounded ontologies of increasing novelty and complexity. En…
Mapping Hearthstone Deck Spaces through MAP-Elites with Sliding Boundaries
Matthew C. Fontaine, Scott Lee, L. B. Soros +3
Quality diversity (QD) algorithms such as MAP-Elites have emerged as a powerful alternative to traditional single-objective optimization methods. They were initially applied to evo…