Session: 20-17-01: Rising Stars of Mechanical Engineering

Paper Number: 172845

Problem Partitioning and Division of Labor for Human-Computer Collaboration in Engineering Design 

The objective of this Faculty Early Career Development (CAREER) research project is to understand how the allocation of collective design tasks into teams of humans and Artificial Intelligence (AI)-enabled computer agents with diverse decision-making characteristics affect design outcomes. The overarching premise of this research is that the current organization of design teams around analysis disciplines (e.g., thermal analysis, structural analysis) or around physical components (e.g., engine, battery, exterior body) may not be the ideal way to architect human-AI teams. Considering the differences between humans and AI systems in terms of their capabilities, there may be alternative ways to divide tasks and responsibilities in system-level design problems among humans and artificial members within a team. This study contributes to the field of design science through a systematic and comprehensive analysis of team architectures in hybrid human-AI teams and the impact of those different architectures on design outcomes. Rather than assuming a pre-defined role for AI as frequently seen in existing literature, this CAREER project follows a top-down systems engineering approach to identify best practices for defining roles for AI in a design team using computational models of human and AI decision-making processes. Specifically, the proposed approach addresses the lack of fundamental principles to guide task partitioning and division of labor for hybrid human-AI teams, accounting not only for heterogeneity among decision-makers (represented by a select set of characteristics), but also for important human factors. This project uses multi-agent simulations to model generalized agents that solve context-free design problems following Bayesian decision-making processes. These agents are characterized in terms of task performance, self-confidence, and confidence in other team members. The computational analysis uses various problem partitioning and task assignment strategies from decomposition-based design optimization and machine learning to quantify their impact on team collaboration considering diverse agent characteristics. Mirroring this simulation scenario, behavioral experiments on an electric vehicle design and control game presents a collaborative design decision-making problem for hybrid human-AI teams with alternative task allocation scenarios in a controlled setting. These experiments collect behavioral data that capture the effects of human factors, including bias, workload, and job satisfaction to compare with computational analysis and validate or refine the computational findings. The findings of this CAREER project will inform how AI technology should be integrated into the engineering design workforce with proper task allocation in order to reduce system development time and costs for future enterprises in multiple industries, spanning from smart healthcare to defense.

Presenting Author: Alparslan Emrah Bayrak Lehigh University

Presenting Author Biography: A. Emrah Bayrak is an Assistant Professor in the Department of Mechanical Engineering and Mechanics at Lehigh University. He received his B.S. degree (2011) in mechatronics engineering from Sabanci University, M.S. (2013) and PhD degrees (2015) in mechanical engineering from the University of Michigan. Prior to joining Lehigh, he was an Assistant Professor in the School of Systems and Enterprises at Stevens Institute of Technology. Dr. Bayrak’s research focuses on integrating computational methods with human cognition for the design and control of smart products and systems. He is particularly interested in developing artificial intelligence (AI) systems that can effectively collaborate with humans considering unique capabilities of humans and computational systems. He studies the impact of AI behaviors, division of labor and coordination on trust and performance in human-AI collaboration. His research uses methods from design, controls and machine learning as well as human-subject experiments on virtual environments such as video games.

Authors:

Alparslan Emrah Bayrak Lehigh University