Computationally Facilitating the Problem-Solving Design Process Via Real-Time Process Management

Teams are a major facet of engineering and are commonly thought to be necessary when solving dynamic and complex problems, such as engineering design tasks. Even though teams collectively bring a diversity of knowledge and perspectives to problem solving, previous work has demonstrated that in certain scenarios, such as in language-based and configuration design problems, the production by a team is inferior to that of a similar number of individuals solving independently (i.e., nominal teams). Aid in the form of design stimuli catalyze group creativity and help designers overcome impasses. However, methods for applying stimuli in the engineering design literature are largely static; they do not adapt to the dynamics of either the designer or the design process, both of which evolve throughout the problem-solving process. Thus, the overarching goal of this dissertation is to explore, better understand, and facilitate problem solving computationally, via adaptive, process management. 

This dissertation first compares individual versus group problem solving within the domain of engineering design. Through a behavioral study, our results corroborate previous findings, exhibiting that individuals outperform teams in the overall quality of their design solutions, even in this more free-flowing and explorative setting of conceptual design. Exploiting this result, we consider and explore whether a human, process manager can lessen this underperformance of design teams compared to nominal teams, and help teams overcome potential deterrents that may be contributing to their inferior performance. The managerial interactions with the design teams are investigated and post-study interviews with the human process managers are conducted, in an attempt to uncover some of the cognitive rationale and strategies that may be beneficial throughout problem solving. Motivated from these post-study interviews, a topic-modeling approach then analyzes team cognition and the impact of these process manager interventions. The results from this approach show that the impacts of these interventions can be computationally detected through team discourse. Overall, these studies provide a conceptual basis for the detection and facilitation of design interventions based on real-time, discourse data. 

Next, two novel frameworks are studied, both of which take steps towards tracking features of design teams and utilizing that information to intervene. The first study analyzes the impact of modulating the distance of design stimuli from a designers’ current state, in this case, their current design solution, within a broader design space. Utilizing semantic comparisons between their current solution and a broad database of related example solutions, designers receive computationally selected inspirational stimuli midway through a problem-solving session. Through a regression analysis, the results exhibit increased performance when capturing their design state and providing increased stimulus quality. The second framework creates an artificial intelligent process manager agent to manage the design process of engineering teams in real-time, tracking features of teams’ actions and communications during a complex design and pathplanning task with multidisciplinary team members. Teams are also placed under the guidance of human process managers for comparison. Across several dimensions, the overall results show that the AI manager agent introduced matches the capabilities of the human managers, showing potential in automating the management of a complex design process. Before and after analyses of the interventions indicate mixed adherence to the different types of interventions as induced in the intended process changes in the teams, and regression analyses show the impact of different interventions. Overall, this dissertation lays the groundwork for a computational development and deployment of adaptive process management, with the hope to make engineering designs as efficient as possible.