Lode Enhancer: Level Co-creation Through Scaling
- URL: http://arxiv.org/abs/2308.01543v1
- Date: Thu, 3 Aug 2023 05:23:07 GMT
- Title: Lode Enhancer: Level Co-creation Through Scaling
- Authors: Debosmita Bhaumik, Julian Togelius, Georgios N. Yannakakis, Ahmed
Khalifa
- Abstract summary: We explore AI-powered upscaling as a design assistance tool in the context of creating 2D game levels.
Deep neural networks are used to upscale artificially downscaled patches of levels from the puzzle platformer game Lode Runner.
- Score: 6.739485960737326
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We explore AI-powered upscaling as a design assistance tool in the context of
creating 2D game levels. Deep neural networks are used to upscale artificially
downscaled patches of levels from the puzzle platformer game Lode Runner. The
trained networks are incorporated into a web-based editor, where the user can
create and edit levels at three different levels of resolution: 4x4, 8x8, and
16x16. An edit at any resolution instantly transfers to the other resolutions.
As upscaling requires inventing features that might not be present at lower
resolutions, we train neural networks to reproduce these features. We introduce
a neural network architecture that is capable of not only learning upscaling
but also giving higher priority to less frequent tiles. To investigate the
potential of this tool and guide further development, we conduct a qualitative
study with 3 designers to understand how they use it. Designers enjoyed
co-designing with the tool, liked its underlying concept, and provided feedback
for further improvement.
Related papers
- PonderV2: Pave the Way for 3D Foundation Model with A Universal
Pre-training Paradigm [114.47216525866435]
We introduce a novel universal 3D pre-training framework designed to facilitate the acquisition of efficient 3D representation.
For the first time, PonderV2 achieves state-of-the-art performance on 11 indoor and outdoor benchmarks, implying its effectiveness.
arXiv Detail & Related papers (2023-10-12T17:59:57Z) - Total Variation Optimization Layers for Computer Vision [130.10996341231743]
We propose total variation (TV) minimization as a layer for computer vision.
Motivated by the success of total variation in image processing, we hypothesize that TV as a layer provides useful inductive bias for deep-nets.
We study this hypothesis on five computer vision tasks: image classification, weakly supervised object localization, edge-preserving smoothing, edge detection, and image denoising.
arXiv Detail & Related papers (2022-04-07T17:59:27Z) - Level generation and style enhancement -- deep learning for game
development overview [0.0]
We present seven approaches to create level maps, each using statistical methods, machine learning, or deep learning.
We aim to present new possibilities for game developers and level artists.
arXiv Detail & Related papers (2021-07-15T15:24:43Z) - Deep Policy Networks for NPC Behaviors that Adapt to Changing Design
Parameters in Roguelike Games [137.86426963572214]
Turn-based strategy games like Roguelikes, for example, present unique challenges to Deep Reinforcement Learning (DRL)
We propose two network architectures to better handle complex categorical state spaces and to mitigate the need for retraining forced by design decisions.
arXiv Detail & Related papers (2020-12-07T08:47:25Z) - Mixed-Initiative Level Design with RL Brush [8.979403815167178]
This paper introduces RL Brush, a level-editing tool for tile-based games designed for mixed-initiative co-creation.
The tool uses reinforcement-learning-based models to augment manual human level-design through the addition of AI-generated suggestions.
arXiv Detail & Related papers (2020-08-06T17:25:14Z) - Shape Adaptor: A Learnable Resizing Module [59.940372879848624]
We present a novel resizing module for neural networks: shape adaptor, a drop-in enhancement built on top of traditional resizing layers.
Our implementation enables shape adaptors to be trained end-to-end without any additional supervision.
We show the effectiveness of shape adaptors on two other applications: network compression and transfer learning.
arXiv Detail & Related papers (2020-08-03T14:15:52Z) - Improving Target-driven Visual Navigation with Attention on 3D Spatial
Relationships [52.72020203771489]
We investigate target-driven visual navigation using deep reinforcement learning (DRL) in 3D indoor scenes.
Our proposed method combines visual features and 3D spatial representations to learn navigation policy.
Our experiments, performed in the AI2-THOR, show that our model outperforms the baselines in both SR and SPL metrics.
arXiv Detail & Related papers (2020-04-29T08:46:38Z) - Interactive Evolution and Exploration Within Latent Level-Design Space
of Generative Adversarial Networks [8.091708140619946]
Latent Variable Evolution (LVE) has recently been applied to game levels.
This paper introduces a tool for interactive LVE of tile-based levels for games.
The tool also allows for direct exploration of the latent dimensions, and allows users to play discovered levels.
arXiv Detail & Related papers (2020-03-31T22:52:17Z) - Binary Neural Networks: A Survey [126.67799882857656]
The binary neural network serves as a promising technique for deploying deep models on resource-limited devices.
The binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network.
We present a survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error.
arXiv Detail & Related papers (2020-03-31T16:47:20Z) - Learning to Generate Levels From Nothing [5.2508303190856624]
We propose Generative Playing Networks which design levels for itself to play.
The algorithm is built in two parts; an agent that learns to play game levels, and a generator that learns the distribution of playable levels.
We demonstrate the capability of this framework by training an agent and level generator for a 2D dungeon crawler game.
arXiv Detail & Related papers (2020-02-12T22:07:23Z) - PCGRL: Procedural Content Generation via Reinforcement Learning [6.32656340734423]
We investigate how reinforcement learning can be used to train level-designing agents in games.
By seeing the design problem as a sequential task, we can use reinforcement learning to learn how to take the next action.
This approach can be used when few or no examples exist to train from, and the trained generator is very fast.
arXiv Detail & Related papers (2020-01-24T22:09:08Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.