Human and Computational Approaches for Design Problem-Solving

Abstract

Human and computational approaches are both commonly used to solve design problems, and each offers unique advantages. Human designers may draw upon their expertise, intuition, and creativity, while computational approaches are used to algorithmically configure and evaluate design alternatives quickly. It is possible to leverage the advantages of each with a human-in-the-loop design approach, which relies on human designers guiding computational processes; empirical design research for better understanding human designers' strengths and limitations can inform the development human-in-the-loop design approaches. In this chapter, the advantages of human and computational design processes are outlined, in addition to how they are researched. An empirical research example is provided for conducting human participant experiments and simulating human design problem-solving strategies with software agent simulations that are used to develop improved strategies. The chapter concludes by discussing general considerations in human and computational research, and their role in developing new human-in-the-loop design processes for complex engineering applications.

Complexity drivers in engineering design

“…The second point is that among all of the research contributions and advancement developed, there is no paper that deals with the global complexity of the design process and especially with the organization management based on the CAS perspective. Some researchers develop models based on multi-agent system: First, to simulate human design problem-solving strategies with software agent simulations that are used to improve strategies that include defining an experiment, developing a method for carrying out an experiment and measuring (Egan and Jonathan, 2016); second, to simulate the formation of transactive memory through social learning from direct and indirect communications (Singh et al, 2013); third, to capture the distributed nature of complex systems design by decomposing the ability to control design variables using a multi-agent learning system (Hulse, 2019); and fourth, to examine how the properties of configuration design problems can be leveraged to select the best values for team characteristics. However, to deal with all aspects of CAS in the design process, MAS must deal with multiple levels of abstractions and openness, which is not the case for most solutions (Gilbert, 2008).…”

Material Balance

“…Computer-human integration will enable forms of expression unique to emerge (ibid.). According to Egan and Cagan (2016) human designers may draw upon their expertise, intuition, and creativity, while computational approaches are used to algorithmically configure and evaluate design alternatives quickly. Thus, new technologies are not to be isolated from traditional media, as innovation may arise by the combination of analogue and digital techniques and design methodology (Symeonidou 2018).…”

Learning an Optimization Algorithm Through Human Design Iterations

“…It is important to note that the focus of this paper is on the design of optimization algorithms aided by human demonstrations, rather than the derivation of qualitative explanations of the strengths and limitations of human design strategies. There have been numerous studies from the latter category in recent years (see [23][24][25][26][27][28] for example). This paper is also distinguished from studies that propose human-inspired optimization algorithms (see [29][30][31] for example), in that the learning of the optimization algorithm in our case is conducted by another algorithm, rather than by human researchers.…”