Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach

• URL: http://arxiv.org/abs/2309.13071v1
• Date: Mon, 18 Sep 2023 20:39:14 GMT
• Title: Tree-Based Reconstructive Partitioning: A Novel Low-Data Level Generation Approach
• Authors: Emily Halina and Matthew Guzdial
• Abstract summary: Procedural Content Generation (PCG) and Machine Learning (PCGML) have appeared in published games. Tree-based Reconstructive Partitioning (TRP) is a novel PCGML approach aimed to address this problem.
• Score: 5.626364462708322
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• GAMformer: In-Context Learning for Generalized Additive Models [53.08263343627232]
  We introduce GAMformer, the first method to leverage in-context learning to estimate shape functions of a GAM in a single forward pass. Our experiments show that GAMformer performs on par with other leading GAMs across various classification benchmarks.
  arXiv  Detail & Related papers  (2024-10-06T17:28:20Z)
• PCGRL+: Scaling, Control and Generalization in Reinforcement Learning Level Generators [2.334978724544296]
  Procedural Content Generation via Reinforcement Learning (PCGRL) has been introduced as a means by which controllable designer agents can be trained. PCGRL offers a unique set of affordances for game designers, but it is constrained by the compute-intensive process of training RL agents. We implement several PCGRL environments in Jax so that all aspects of learning and simulation happen in parallel on the GPU.
  arXiv  Detail & Related papers  (2024-08-22T16:30:24Z)
• Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference [4.913049603343811]
  Existing r measurement methods often overlook the incremental learning scenario. Most existing class incremental learning approaches are unsuitable for r measurement. We present a novel method named ADDP to tackle continual learning for r measurement.
  arXiv  Detail & Related papers  (2024-07-19T01:49:09Z)
• G-PCGRL: Procedural Graph Data Generation via Reinforcement Learning [0.28273304533873334]
  In games, graph-based data structures are omnipresent and represent game economies, skill trees or complex, branching quest lines. We propose a novel and controllable method for the procedural generation of graph data using reinforcement learning. Our method is capable of generating graph-based content quickly and reliably to support and inspire designers in the game creation process.
  arXiv  Detail & Related papers  (2024-07-15T07:11:00Z)
• Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction [53.88231294380083]
  We introduce a novel Multi-Epoch learning with Data Augmentation (MEDA) framework, suitable for both non-continual and continual learning scenarios. MEDA minimizes overfitting by reducing the dependency of the embedding layer on subsequent training data. Our findings confirm that pre-trained layers can adapt to new embedding spaces, enhancing performance without overfitting.
  arXiv  Detail & Related papers  (2024-06-27T04:00:15Z)
• PCGPT: Procedural Content Generation via Transformers [1.515687944002438]
  The paper presents the PCGPT framework, an innovative approach to procedural content generation (PCG) using offline reinforcement learning and transformer networks. PCGPT utilizes an autoregressive model based on transformers to generate game levels iteratively, addressing the challenges of traditional PCG methods such as repetitive, predictable, or inconsistent content.
  arXiv  Detail & Related papers  (2023-10-03T19:58:02Z)
• Pair then Relation: Pair-Net for Panoptic Scene Graph Generation [54.92476119356985]
  Panoptic Scene Graph (PSG) aims to create a more comprehensive scene graph representation using panoptic segmentation instead of boxes. Current PSG methods have limited performance, which hinders downstream tasks or applications. We present a novel framework: Pair then Relation (Pair-Net), which uses a Pair Proposal Network (PPN) to learn and filter sparse pair-wise relationships between subjects and objects.
  arXiv  Detail & Related papers  (2023-07-17T17:58:37Z)
• Procedural Content Generation via Knowledge Transformation (PCG-KT) [8.134009219520289]
  We introduce the concept of Procedural Content Generation via Knowledge Transformation (PCG-KT) Our work is motivated by a substantial number of recent PCG works that focus on generating novel content via repurposing derived knowledge.
  arXiv  Detail & Related papers  (2023-05-01T03:31:22Z)
• On-Device Domain Generalization [93.79736882489982]
  Domain generalization is critical to on-device machine learning applications. We find that knowledge distillation is a strong candidate for solving the problem. We propose a simple idea called out-of-distribution knowledge distillation (OKD), which aims to teach the student how the teacher handles (synthetic) out-of-distribution data.
  arXiv  Detail & Related papers  (2022-09-15T17:59:31Z)
• Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data [125.7135706352493]
  Generative adversarial networks (GANs) typically require ample data for training in order to synthesize high-fidelity images. Recent studies have shown that training GANs with limited data remains formidable due to discriminator overfitting. This paper introduces a novel strategy called Adaptive Pseudo Augmentation (APA) to encourage healthy competition between the generator and the discriminator.
  arXiv  Detail & Related papers  (2021-11-12T18:13:45Z)
• Prototypical Contrastive Learning of Unsupervised Representations [171.3046900127166]
  Prototypical Contrastive Learning (PCL) is an unsupervised representation learning method. PCL implicitly encodes semantic structures of the data into the learned embedding space. PCL outperforms state-of-the-art instance-wise contrastive learning methods on multiple benchmarks.
  arXiv  Detail & Related papers  (2020-05-11T09:53:36Z)

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.