143 citations · 546 across the 35 of their papers we have counts for
7 papers · 1 filter
A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
Matthew Stephenson, Damien Anderson, Ahmed Khalifa +4
This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms. This method w…
Generating Levels That Teach Mechanics
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +2
The automatic generation of game tutorials is a challenging AI problem. While it is possible to generate annotations and instructions that explain to the player how the game is pla…
AtDelfi: Automatically Designing Legible, Full Instructions For Games
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +3
This paper introduces a fully automatic method for generating video game tutorials. The AtDELFI system (AuTomatically DEsigning Legible, Full Instructions for games) was created to…
Talakat: Bullet Hell Generation through Constrained Map-Elites
Ahmed Khalifa, Scott Lee, Andy Nealen +1
We describe a search-based approach to generating new levels for bullet hell games, which are action games characterized by and requiring avoidance of a very large amount of projec…
Illuminating Generalization in Deep Reinforcement Learning through Procedural Level Generation
Niels Justesen, Ruben Rodriguez Torrado, Philip Bontrager +3
Deep reinforcement learning (RL) has shown impressive results in a variety of domains, learning directly from high-dimensional sensory streams. However, when neural networks are tr…
"Press Space to Fire": Automatic Video Game Tutorial Generation
Michael Cerny Green, Ahmed Khalifa, Gabriella A. B. Barros +1
We propose the problem of tutorial generation for games, i.e. to generate tutorials which can teach players to play games, as an AI problem. This problem can be approached in sever…