143 citations · 546 across the 35 of their papers we have counts for
6 papers · 2 filters
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…
"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…
General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms
Diego Perez-Liebana, Jialin Liu, Ahmed Khalifa +3
General Video Game Playing (GVGP) aims at designing an agent that is capable of playing multiple video games with no human intervention. In 2014, The General Video Game AI (GVGAI)…