54 research outputs found
Transfer Learning for Underrepresented Music Generation
This paper investigates a combinational creativity approach to transfer
learning to improve the performance of deep neural network-based models for
music generation on out-of-distribution (OOD) genres. We identify Iranian folk
music as an example of such an OOD genre for MusicVAE, a large generative music
model. We find that a combinational creativity transfer learning approach can
efficiently adapt MusicVAE to an Iranian folk music dataset, indicating
potential for generating underrepresented music genres in the future.Comment: 5 pages, 3 figures, International Conference on Computational
Creativit
Automatic Mapping of NES Games with Mappy
Game maps are useful for human players, general-game-playing agents, and
data-driven procedural content generation. These maps are generally made by
hand-assembling manually-created screenshots of game levels. Besides being
tedious and error-prone, this approach requires additional effort for each new
game and level to be mapped. The results can still be hard for humans or
computational systems to make use of, privileging visual appearance over
semantic information. We describe a software system, Mappy, that produces a
good approximation of a linked map of rooms given a Nintendo Entertainment
System game program and a sequence of button inputs exploring its world. In
addition to visual maps, Mappy outputs grids of tiles (and how they change over
time), positions of non-tile objects, clusters of similar rooms that might in
fact be the same room, and a set of links between these rooms. We believe this
is a necessary step towards developing larger corpora of high-quality
semantically-annotated maps for PCG via machine learning and other
applications.Comment: 9 pages, 7 figures. Appearing at Procedural Content Generation
Workshop 201
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AI
In fighting games, individual players of the same skill level often exhibit
distinct strategies from one another through their gameplay. Despite this, the
majority of AI agents for fighting games have only a single strategy for each
"level" of difficulty. To make AI opponents more human-like, we'd ideally like
to see multiple different strategies at each level of difficulty, a concept we
refer to as "multidimensional" difficulty. In this paper, we introduce a
diversity-based deep reinforcement learning approach for generating a set of
agents of similar difficulty that utilize diverse strategies. We find this
approach outperforms a baseline trained with specialized, human-authored reward
functions in both diversity and performance.Comment: 8 pages, 2 figures, Experimental AI in Games 202
Joint Level Generation and Translation Using Gameplay Videos
Procedural Content Generation via Machine Learning (PCGML) faces a
significant hurdle that sets it apart from other fields, such as image or text
generation, which is limited annotated data. Many existing methods for
procedural level generation via machine learning require a secondary
representation besides level images. However, the current methods for obtaining
such representations are laborious and time-consuming, which contributes to
this problem. In this work, we aim to address this problem by utilizing
gameplay videos of two human-annotated games to develop a novel multi-tail
framework that learns to perform simultaneous level translation and generation.
The translation tail of our framework can convert gameplay video frames to an
equivalent secondary representation, while its generation tail can produce
novel level segments. Evaluation results and comparisons between our framework
and baselines suggest that combining the level generation and translation tasks
can lead to an overall improved performance regarding both tasks. This
represents a possible solution to limited annotated level data, and we
demonstrate the potential for future versions to generalize to unseen games.Comment: 8 pages, 4 figure
Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach
Procedural Content Generation (PCG) is the algorithmic generation of content,
often applied to games. PCG and PCG via Machine Learning (PCGML) have appeared
in published games. However, it can prove difficult to apply these approaches
in the early stages of an in-development game. PCG requires expertise in
representing designer notions of quality in rules or functions, and PCGML
typically requires significant training data, which may not be available early
in development. In this paper, we introduce Tree-based Reconstructive
Partitioning (TRP), a novel PCGML approach aimed to address this problem. Our
results, across two domains, demonstrate that TRP produces levels that are more
playable and coherent, and that the approach is more generalizable with less
training data. We consider TRP to be a promising new approach that can afford
the introduction of PCGML into the early stages of game development without
requiring human expertise or significant training data.Comment: 9 pages, 3 figures, The 19th AAAI Conference on Artificial
Intelligence and Interactive Digital Entertainment (AIIDE 2023
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