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Matthew Guzdial

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

IJCAI Conference 2024 Conference Paper

Human-AI Interaction Generation: A Connective Lens for Generative AI and Procedural Content Generation

Matthew Guzdial

Generative AI has recently gained popularity as a paradigm for content generation. In this paper, we link this paradigm to an older one: Procedural Content Generation (PCG). We propose a lens to identify the commonalities between both paradigms that we call human-AI interactive generation. Using this lens, we identify three beneficial attributes then survey recent related work and summarize relevant findings.

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IJCAI Conference 2022 Conference Paper

Threshold Designer Adaptation: Improved Adaptation for Designers in Co-creative Systems

Emily Halina
Matthew Guzdial

To best assist human designers with different styles, Machine Learning (ML) systems need to be able to adapt to them. However, there has been relatively little prior work on how and when to best adapt an ML system to a co-designer. In this paper we present threshold designer adaptation: a novel method for adapting a creative ML model to an individual designer. We evaluate our approach with a human subject study using a co-creative rhythm game design tool. We find that designers prefer our proposed method and produce higher quality content in comparison to an existing baseline.

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IJCAI Conference 2017 Conference Paper

Game Engine Learning from Video

Matthew Guzdial
Boyang Li
Mark O. Riedl

Intelligent agents need to be able to make predictions about their environment. In this work we present a novel approach to learn a forward simulation model via simple search over pixel input. We make use of a video game, Super Mario Bros. , as an initial test of our approach as it represents a physics system that is significantly less complex than reality. We demonstrate the significant improvement of our approach in predicting future states compared with a baseline CNN and apply the learned model to train a game playing agent. Thus we evaluate the algorithm in terms of the accuracy and value of its output model.

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