Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques

• URL: http://arxiv.org/abs/2204.07641v1
• Date: Fri, 15 Apr 2022 20:40:43 GMT
• Title: Investigating Positive and Negative Qualities of Human-in-the-Loop Optimization for Designing Interaction Techniques
• Authors: Liwei Chan, Yi-Chi Liao, George B. Mo, John J. Dudley, Chun-Lien Cheng, Per Ola Kristensson, Antti Oulasvirta
• Abstract summary: Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. Model-based computational design algorithms assist designers by generating design examples during design. Black box methods for assistance, on the other hand, can work with any design problem.
• Score: 55.492211642128446
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives. In HCI, design optimization problems are often exceedingly complex, involving multiple objectives and expensive empirical evaluations. Model-based computational design algorithms assist designers by generating design examples during design, however they assume a model of the interaction domain. Black box methods for assistance, on the other hand, can work with any design problem. However, virtually all empirical studies of this human-in-the-loop approach have been carried out by either researchers or end-users. The question stands out if such methods can help designers in realistic tasks. In this paper, we study Bayesian optimization as an algorithmic method to guide the design optimization process. It operates by proposing to a designer which design candidate to try next, given previous observations. We report observations from a comparative study with 40 novice designers who were tasked to optimize a complex 3D touch interaction technique. The optimizer helped designers explore larger proportions of the design space and arrive at a better solution, however they reported lower agency and expressiveness. Designers guided by an optimizer reported lower mental effort but also felt less creative and less in charge of the progress. We conclude that human-in-the-loop optimization can support novice designers in cases where agency is not critical.

Related papers

• I-Design: Personalized LLM Interior Designer [57.00412237555167]
  I-Design is a personalized interior designer that allows users to generate and visualize their design goals through natural language communication. I-Design starts with a team of large language model agents that engage in dialogues and logical reasoning with one another. The final design is then constructed in 3D by retrieving and integrating assets from an existing object database.
  arXiv  Detail & Related papers  (2024-04-03T16:17:53Z)
• Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
  We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization. We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons. Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
  arXiv  Detail & Related papers  (2024-03-20T00:41:12Z)
• Compositional Generative Inverse Design [69.22782875567547]
  Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem. We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples. In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
  arXiv  Detail & Related papers  (2024-01-24T01:33:39Z)
• ECoDe: A Sample-Efficient Method for Co-Design of Robotic Agents [11.449817465618658]
  Co-designing autonomous robotic agents involves simultaneously optimizing the controller and physical design of the agent. This can be challenging when the design space is large. We propose a multi-fidelity-based exploration strategy to improve the sample efficiency of co-design.
  arXiv  Detail & Related papers  (2023-09-08T02:54:31Z)
• Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimization [101.18253437732933]
  Quality diversity algorithms can be used to create a diverse set of solutions to inform engineers' intuition. But quality diversity is not efficient in very expensive problems, needing 100.000s of evaluations. We show that we can produce better machine learning models by producing training data with quality diversity.
  arXiv  Detail & Related papers  (2023-03-28T11:20:59Z)
• Fusion of ML with numerical simulation for optimized propeller design [0.6767885381740952]
  We propose an alternative way to use ML model to surrogate the design process. By using this trained surrogate model with the traditional optimization method, we can get the best of both worlds. Empirical evaluations of propeller design problems show that a better efficient design can be found in fewer evaluations using SAO.
  arXiv  Detail & Related papers  (2023-02-28T16:42:07Z)
• Design Space Exploration and Explanation via Conditional Variational Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
  This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE) The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
  arXiv  Detail & Related papers  (2022-11-29T17:28:31Z)
• Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs [5.8010446129208155]
  This study proposes a reinforcement learning based generative design process, with reward functions maximizing the diversity of topology designs. We show that RL-based generative design produces a large number of diverse designs within a short inference time by exploiting GPU in a fully automated manner.
  arXiv  Detail & Related papers  (2020-08-17T06:50:47Z)
• Computational Design with Crowds [34.76077954140923]
  Computational design is aimed at supporting or automating design processes using computational techniques. One promising approach is to leverage human computation; that is, to incorporate human input into the process. We discuss such computational design with crowds in the domain of parameter working system.
  arXiv  Detail & Related papers  (2020-02-20T10:40:13Z)

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.