109 citations · 277 across the 50 of their papers we have counts for
7 papers · 1 filter
Towards Automated Let's Play Commentary
Matthew Guzdial, Shukan Shah, Mark Riedl
We introduce the problem of generating Let's Play-style commentary of gameplay video via machine learning. We propose an analysis of Let's Play commentary and a framework for build…
Co-Creative Level Design via Machine Learning
Matthew Guzdial, Nicholas Liao, Mark Riedl
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. For c…
Explainable PCGML via Game Design Patterns
Matthew Guzdial, Joshua Reno, Jonathan Chen +2
Procedural content generation via Machine Learning (PCGML) is the umbrella term for approaches that generate content for games via machine learning. One of the benefits of PCGML is…
Player Experience Extraction from Gameplay Video
Zijin Luo, Matthew Guzdial, Nicholas Liao +1
The ability to extract the sequence of game events for a given player's play-through has traditionally required access to the game's engine or source code. This serves as a barrier…
Automated Game Design via Conceptual Expansion
Matthew Guzdial, Mark Riedl
Automated game design has remained a key challenge within the field of Game AI. In this paper, we introduce a method for recombining existing games to create new games through a pr…
Creative Invention Benchmark
Matthew Guzdial, Nicholas Liao, Vishwa Shah +1
In this paper we present the Creative Invention Benchmark (CrIB), a 2000-problem benchmark for evaluating a particular facet of computational creativity. Specifically, we address c…
Combinets: Creativity via Recombination of Neural Networks
Matthew Guzdial, Mark O. Riedl
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge. In comparison, deep neural ne…