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20042025
most citedDeep Learning for Procedural Content Generation

143 citations · 1.3k across the 89 of their papers we have counts for

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Showing 2019 · cs.AIShow all

16 papers · 2 filters

cs.AI2019

Increasing Generality in Machine Learning through Procedural Content Generation

Sebastian Risi, Julian Togelius

Procedural Content Generation (PCG) refers to the practice, in videogames and other games, of generating content such as levels, quests, or characters algorithmically. Motivated by…

cs.AI2019

Automatic Critical Mechanic Discovery Using Playtraces in Video Games

Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2

We present a new method of automatic critical mechanic discovery for video games using a combination of game description parsing and playtrace information. This method is applied t…

cs.AI2019

Evaluation of a Recommender System for Assisting Novice Game Designers

Tiago Machado, Daniel Gopstein, Oded Nov +3

Game development is a complex task involving multiple disciplines and technologies. Developers and researchers alike have suggested that AI-driven game design assistants may improv…

cs.AI2019

Automated Playtesting of Matching Tile Games

Luvneesh Mugrai, Fernando de Mesentier Silva, Christoffer Holmgård +1

Matching tile games are an extremely popular game genre. Arguably the most popular iteration, Match-3 games, are simple to understand puzzle games, making them great benchmarks for…

cs.AI2019

The Many AI Challenges of Hearthstone

Amy K. Hoover, Julian Togelius, Scott Lee +1

Games have benchmarked AI methods since the inception of the field, with classic board games such as Chess and Go recently leaving room for video games with related yet different s…

cs.AI2019

Diverse Agents for Ad-Hoc Cooperation in Hanabi

Rodrigo Canaan, Julian Togelius, Andy Nealen +1

In complex scenarios where a model of other actors is necessary to predict and interpret their actions, it is often desirable that the model works well with a wide variety of previ…