OPTIMISM: Enabling Collaborative Implementation of Domain-Specific Metaheuristic Optimization

要旨

For non-technical domain experts and designers it can be a substantial challenge to create designs that meet domain specific goals. This presents an opportunity to create specialized tools that produce optimized designs in the domain. However, implementing domain-specific optimization methods requires a rare combination of programming and domain expertise. Creating flexible design tools with re-configurable optimizers that can tackle a variety of problems in a domain requires even more domain and programming expertise. We present OPTIMISM, a toolkit which enables programmers and domain experts to collaboratively implement an optimization component of design tools. OPTIMISM supports the implementation of metaheuristic optimization methods by factoring them into easy to implement and reuse components: objectives that measure desirable qualities in the domain, modifiers which make useful changes to designs, design and modifier selectors which determine how the optimizer steps through the search space, and stopping criteria that determine when to return results. Implementing optimizers with OPTIMISM shifts the burden of domain expertise from programmers to domain experts.

著者
Megan Hofmann
Northeastern University, Boston, Massachusetts, United States
Nayha Auradkar
University of Washington, Seattle, Washington, United States
Jessica Birchfield
University of Washington, Seattle, Washington, United States
Jerry Cao
University of Washington, Seattle, Washington, United States
Autumn G. Hughes
Johns Hopkins University, Baltimore, Maryland, United States
Gene S-H. Kim
Stanford University, Stanford, California, United States
Shriya Kurpad
University of Washington, Seattle, Washington, United States
Kathryn J. Lum
University of Washington, Seattle, Washington, United States
Kelly Mack
University of Washington, Seattle, Washington, United States
Anisha Nilakantan
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
Margaret Ellen. Seehorn
Grinnell College, Grinnell, Iowa, United States
Emily Warnock
University of Washington, Seattle, Washington, United States
Jennifer Mankoff
University of Washington, Seattle, Washington, United States
Scott E. Hudson
Carnegie Mellon University, Pittsburgh, Pennsylvania, United States
論文URL

https://doi.org/10.1145/3544548.3580904

動画

会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2023.acm.org/)

セッション: Supporting users in AR and VR

Hall F
6 件の発表
2023-04-26 18:00:00
2023-04-26 19:30:00