Knowledge-based and Data-driven Integrating Methodologies for Collective Intelligence Decision Making: A Survey

Collective intelligence (CI) shows promising application prospects. Current research methodologies of intelligent decision making for CI systems can be categorized as knowledge-based and data-driven methods, bothshowing inherent advantages and disadvantages. Therefore, we claim that integrating knowledge-based and datadriven paradigms offers a new and prospective research direction. In this paper, possible methods of this integration are systematically introduced, and all of these methods are classified into a framework level and an algorithm level. Specifically, the methods integrated in the algorithm level are further categorized as hierarchical and componentized methods. In the hierarchical taxonomy, neural network tree, genetic fuzzy tree, and hierarchical reinforcement learning are included. In the componentized taxonomy, knowledge enhanced data-driven, data optimized knowledgedriven, and complementary knowledge and data driven methods are introduced. Finally, several future research priorities on the knowledge-based and data-driven integrating paradigms are proposed for the considerations of theoretical development and application requirement.