The Impact of Visualizing Design Gradients for Human Designers

• URL: http://arxiv.org/abs/2110.04147v1
• Date: Thu, 7 Oct 2021 05:41:09 GMT
• Title: The Impact of Visualizing Design Gradients for Human Designers
• Authors: Matthew Guzdial, Nathan Sturtevant and Carolyn Yang
• Abstract summary: This paper introduces a mixed-initiative tool employing Exhaustive PCG ( EPCG) for puzzle level design. We run an online human subject study in which individuals use the tool with an EPCG component turned on or off. Our analysis of the results demonstrates that, although a majority of users did not prefer the tool, it made the level design process significantly easier.
• Score: 7.976180564784953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Mixed-initiative Procedural Content Generation (PCG) refers to tools or systems in which a human designer works with an algorithm to produce game content. This area of research remains relatively under-explored, with the majority of mixed-initiative PCG level design systems using a common set of search-based PCG algorithms. In this paper, we introduce a mixed-initiative tool employing Exhaustive PCG (EPCG) for puzzle level design to further explore mixed-initiative PCG. We run an online human subject study in which individuals use the tool with an EPCG component turned on or off. Our analysis of the results demonstrates that, although a majority of users did not prefer the tool, it made the level design process significantly easier, and that the tool impacted the subjects' design process. This paper describes the study results and draws lessons for mixed-initiative PCG tool design.

Related papers

• Benchmarking End-To-End Performance of AI-Based Chip Placement Algorithms [77.71341200638416]
  ChiPBench is a benchmark designed to evaluate the effectiveness of AI-based chip placement algorithms. We have gathered 20 circuits from various domains (e.g., CPU, GPU, and microcontrollers) for evaluation. Results show that even if intermediate metric of a single-point algorithm is dominant, the final PPA results are unsatisfactory.
  arXiv  Detail & Related papers  (2024-07-03T03:29:23Z)
• Geometric Deep Learning for Computer-Aided Design: A Survey [85.79012726689511]
  This survey offers a comprehensive overview of learning-based methods in computer-aided design. It includes similarity analysis and retrieval, 2D and 3D CAD model synthesis, and CAD generation from point clouds. It provides a complete list of benchmark datasets and their characteristics, along with open-source codes that have propelled research in this domain.
  arXiv  Detail & Related papers  (2024-02-27T17:11:35Z)
• Does mapping elites illuminate search spaces? A large-scale user study of MAP--Elites applied to human--AI collaborative design [0.0]
  The tool investigated is based on an evolutionary algorithm attempting to design a virtual car to travel as far as possible in a fixed time. Participants were able to design their own cars, make recommendations to the algorithm and view sets of recommendations from the algorithm.
  arXiv  Detail & Related papers  (2024-01-30T08:54:46Z)
• Mixed-Variable Global Sensitivity Analysis For Knowledge Discovery And Efficient Combinatorial Materials Design [15.840852223499303]
  We develop the first metamodel-based mixed-variable GSA method. Through numerical case studies, we validate and demonstrate the effectiveness of our proposed method for mixed-variable problems. Our method can utilize sensitivity analysis to navigate the optimization in the many-level large design space.
  arXiv  Detail & Related papers  (2023-10-23T17:29:53Z)
• Discovering General Reinforcement Learning Algorithms with Adversarial Environment Design [54.39859618450935]
  We show that it is possible to meta-learn update rules, with the hope of discovering algorithms that can perform well on a wide range of RL tasks. Despite impressive initial results from algorithms such as Learned Policy Gradient (LPG), there remains a gap when these algorithms are applied to unseen environments. In this work, we examine how characteristics of the meta-supervised-training distribution impact the performance of these algorithms.
  arXiv  Detail & Related papers  (2023-10-04T12:52:56Z)
• LLM-based Interaction for Content Generation: A Case Study on the Perception of Employees in an IT department [85.1523466539595]
  This paper presents a questionnaire survey to identify the intention to use generative tools by employees of an IT company. Our results indicate a rather average acceptability of generative tools, although the more useful the tool is perceived to be, the higher the intention seems to be. Our analyses suggest that the frequency of use of generative tools is likely to be a key factor in understanding how employees perceive these tools in the context of their work.
  arXiv  Detail & Related papers  (2023-04-18T15:35:43Z)
• Machine Learning-Based Multi-Objective Design Exploration Of Flexible Disc Elements [1.5638419778920147]
  This paper showcases Artificial Neural Network (ANN) architecture applied to an engineering design problem to explore and identify improved design solutions. The case problem of this study is the design of flexible disc elements used in disc couplings. To accomplish this objective, we employ ANN coupled with genetic algorithm in the design exploration step to identify designs that meet the specified criteria.
  arXiv  Detail & Related papers  (2023-04-14T16:48:51Z)
• A Reinforcement Learning-assisted Genetic Programming Algorithm for Team Formation Problem Considering Person-Job Matching [70.28786574064694]
  A reinforcement learning-assisted genetic programming algorithm (RL-GP) is proposed to enhance the quality of solutions. The hyper-heuristic rules obtained through efficient learning can be utilized as decision-making aids when forming project teams.
  arXiv  Detail & Related papers  (2023-04-08T14:32:12Z)
• PyExperimenter: Easily distribute experiments and track results [63.871474825689134]
  PyExperimenter is a tool to facilitate the setup, documentation, execution, and subsequent evaluation of results from an empirical study of algorithms. It is intended to be used by researchers in the field of artificial intelligence, but is not limited to those.
  arXiv  Detail & Related papers  (2023-01-16T10:43:02Z)
• Evaluating Mixed-Initiative Procedural Level Design Tools using a Triple-Blind Mixed-Method User Study [0.0]
  A tool which generates levels using interactive evolutionary optimisation was designed for this study. The tool identifies level design patterns in an initial hand-designed map and uses that information to drive an interactive optimisation algorithm. A rigorous user study was designed which compared the experiences of designers using the mixed-initiative tool to designers who were given a tool which provided completely random level suggestions.
  arXiv  Detail & Related papers  (2020-05-15T11:40:53Z)
• Learning the Designer's Preferences to Drive Evolution [0.0]
  We present a data-driven solution that pursues to learn from user generated data in a Quality-Diversity Mixed-Initiative Co-Creativity tool. We aim for increasing the user's agency over the generated content in a way that neither stalls the user-tool reciprocal stimuli loop nor fatigues the user with periodical suggestion handpicking.
  arXiv  Detail & Related papers  (2020-03-06T15:10:09Z)

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.