Combining the strength of centralized control and distributed autonomy for crowdsourcing design: An integrated model of Blackboard and Bayesian network

Abstract

Socialized product design (SPD) mode, enabled by advanced internet technologies and sharing economic trends, has the capability of utilizing the design resources from large numbers of socialized designers (SDs) to carry out the design tasks that used to be participated by only the internal R&D staffs of companies. During SPD projects, different kinds of mechanisms can be applied to organize the SDs. Some of them tend to be centralized control (e.g. crowdsourcing design) and some are more of distributed autonomy (e.g. opensourcing design). Both centralized control and distributed autonomy have their strengths and limitations for SPD. Centralized control enables more organized, focused, and efficient project execution, but it limits the emergence of collective intelligence among the SDs. Distributed autonomy helps to explore the innovation potential of SDs by granting them the freedom of communication and mutual inspiration, but it may cause the problem of unreliable and unpredictable design process. To complement the advantages of centralized control and distributed autonomy in SPD, an integrated model of Blackboard and Bayesian network is established in this paper. The Blackboard, whose Control modules are specially customized for human Knowledge sources, is for guaranteeing overall control of the distributed design process and at the same time permitting certain level of autonomy to the SDs. The Bayesian network, built with an improved Bayesian causal map method, is an embedded Control module of the Blackboard which evaluates design solutions according to the incomplete collective judgments from SDs. The operability of the integrated model has been verified through a case study of 3D printer conceptual design project.

Design for additive manufacturing: A three layered conceptual framework for knowledge-based design

“…This manufacturing efficiency and quality problem management application provide root cause analysis, organization clustering, and management of engineering problems that constrain manufacturing efficiency and quality in specific manufacturing processes. 23,24 The domain expert decision-making application can integrate domain experts’ knowledge to make decisions, control task activities in the decision-making process in a workflow model, and event control-configuration of decision-making activities. The manufacturing data management application extracts knowledge elements from engineering case documents and completes the KG extension and relationships.…”

A collective intelligence oriented three-layer framework for socialized and collaborative product design

“…The second layer uses a customized Blackboard model developed on the basis of our previous work (Yang and Jiang, 2020b) to support the execution process of iteratively generating alternative design solutions for the subtasks from the first layer through orderly interaction and mutual inspiration among human Knowledge sources.…”