Modeling multidisciplinary design with multiagent learning

Abstract

Complex engineered systems design is a collaborative activity. To design a system, experts from the relevant disciplines must work together to create the best overall system from their individual components. This situation is analogous to a multiagent system in which agents solve individual parts of a larger problem in a coordinated way. Current multiagent models of design teams, however, do not capture this distributed aspect of design teams – instead either representing designers as agents which control all variables, measuring organizational outcomes instead of design outcomes, or representing different aspects of distributed design, such as negotiation. This paper presents a new model which captures the distributed nature of complex systems design by decomposing the ability to control design variables to individual computational designers acting on a problem with shared constraints. These designers are represented as a multiagent learning system which is shown to perform similarly to a centralized optimization algorithm on the same domain. When used as a model, this multiagent system is shown to perform better when the level of designer exploration is not decayed but is instead controlled based on the increase of design knowledge, suggesting that designers in multidisciplinary teams should not simply reduce the scope of design exploration over time, but should adapt based on changes in their collective knowledge of the design space. This multiagent system is further shown to produce better-performing designs when computational designers design collaboratively as opposed to independently, confirming the importance of collaboration in complex systems design.

Ethics and Strategy in Decision-Based Design Frameworks: Problems and Solutions

“…Decomposing design to individual designers can cause sub-optimization behaviors in value-driven frameworks even when incentives are aligned (Kannan, 2015) (Hulse et al, 2018), as illustrated in Figure 3. Enable coordination, either between designers or using Multidisciplinary Design Optimization in a computational environment (Kannan, 2015).…”

Designing Like a Human: Simulating Cognitive Style in Teamwork with an Agent-Based Model

“…Engineering teams often divide the design of complex systems into multiple components that are then addressed or solved by specialized team members with corresponding expertise in a specific domain [32,44]. This multi-component approach to complex systems design is analogous to coordination between multiple agents in a computational model: decisions are made with respect to separate components and later aggregated to form a global solution [1].…”

“…This multi-component approach to complex systems design is analogous to coordination between multiple agents in a computational model: decisions are made with respect to separate components and later aggregated to form a global solution [1]. An agentbased model can reflect the way engineering teams solve complex systems in a distributed manner by assigning agents to specialized sub-teams that each control a subset of all design variables for a global objective function [44]. This approach makes it possible to use an agent-based model to rapidly investigate the effects of team structure on a design team's performance.…”

“…Since then, many researchers have developed models with the aim of reflecting some aspect of human behavior or teamwork [49, eg] an agent-based model can design a complex system. Like Hulse et al [44], members of the design team adopt specializations by breaking the problem into separate functional subsystems (e.g., engine, suspension, and brakes).…”

Quantifying the Resilience-Informed Scenario Cost Sum: A Value-Driven Design Approach for Functional Hazard Assessment

“…A key difficulty common to many different design changes is predicting how a change will affect the design and operating costs of the functional model given the different couplings which may occur, for example, due to the working, constructional, and system interrelationships which are developed later in the design process. Such couplings are prevalent in the embodiment design stage of highly coupled engineered systems, and provide significant challenges to design coordination [83].…”