Human-computer hybrid teams can meet challenges in designing complex engineered systems. However, the understanding of interaction in the hybrid teams is lacking. We review the literature and identify four key attributes to construct design research platforms that support multi-phase design, hybrid teams, multiple design scenarios, and data logging. Then, we introduce a platform for unmanned aerial vehicle (UAV) design embodying these attributes. With the platform, experiments can be conducted to study how designers and intelligent computational agents interact, support, and impact each other.

In complex engineering systems, complexity may arise by design, or as a by-product of the system's operation. In either case, the cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multi-agent coordination problem, where component decisions and their interactions lead to global behavior. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm (CCEA) can effectively design a complex engineered system. This paper uses a system model of a Formula SAE racing vehicle to illustrate and simulate the methods and potential results. By designing complex systems with a multi-agent coordination approach, a design methodology can be developed to reduce design uncertainty and provide mechanisms through which the system level impact of decisions can be estimated without explicitly modeling such interactions.

Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an Artificial Intelligent (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams' actions and communications during a complex design and path-planning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends towards even superior performance from the AI-managed teams. The managers' intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from the both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.