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research-article

Transferring Design Strategies From Human To Computer And Across Design Problems

[+] Author and Article Information
Ayush Raina

Mechanical Engineering 5000 Forbes Ave Pittsburgh, PA 15213 araina@andrew.cmu.edu

Jonathan Cagan

Department of Mechanical Engineering Scaife Hall Pittsburgh, PA 15213 cagan@cmu.edu

Christopher McComb

The Pennsylvania State University 213U Hammond Building State College, PA 16802 uum209@psu.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received March 16, 2019; final manuscript received July 5, 2019; published online xx xx, xxxx. Assoc. Editor: Ying Liu.

ASME doi:10.1115/1.4044258 History: Received March 16, 2019; Accepted July 06, 2019

Abstract

Solving any design problem involves planning and strategizing, where intermediate processes are identified and then sequenced. This is an abstract skill that designers learn over time and then use across similar problems. However, this transfer of strategies in design has not been effectively modeled or leveraged within computational agents. This note presents an approach to represent design strategies using a probabilistic model. The model provides a mechanism to generate new designs based on certain design strategies while solving configuration design task in a sequential manner. This work also demonstrates that this probabilistic representation can be used to transfer strategies from human designers to computational design agents in a way that is general and useful. This transfer-driven approach opens up the possibility of identifying high-performing behavior in human designers and using it to guide computational design agents. Finally, a quintessential behavior of transfer learning is illustrated by agents as transferring design strategies across different problems led to an improvement in agent performance. The work presented in this study leverages the CISAT framework, an agent-based model that has been shown to mimic human problem-solving in configuration design problems.

Copyright © 2019 by ASME
Topics: Design , Computers
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