Interoperability and Self-Organization in Human-Machine Collective Intelligence
Alexander V. Smirnov, Andrew Ponomarev · 2021 IEEE International Conference on Systems, Man, and Cybernetics (SMC) · 2021
Collective intelligence helps to address many problems that are intractable by efforts of a single person. One of the current challenges in building collective intelligence systems is to seamlessly integrate AI agents in them, transforming traditional collective intelligence to human-machine collective intelligence. The paper describes an approach to address two cornerstone problems of human-machine collective intelligence – interoperability in hybrid teams and sustaining the degree of self-organization required to address complex problems. To solve the first problem, it is proposed to leverage the expressiveness of ontologies and the technology of ontology-based smart spaces. To address the second one, a concept of assisted self-organization is proposed, including a method to assist in team-formation and a method to detect and resolve non-productive situations in the collective work.