CONCEPTUAL FRAMEWORK OF A HUMAN-MACHINE COLLECTIVE INTELLIGENCE ENVIRONMENT FOR DECISION SUPPORT

Abstract

The paper extends collective intelligence understanding to the problemsolving abilities of heterogeneous groups, consisting of human participants and software services. It describes a conceptual framework of a new computational environment, supporting such heterogeneous teams, working on decision support problems. In particular, the paper discusses the most acute problems, related to such heterogeneous collective intelligence - interoperability and self-organization. To address interoperability issues, the environment relies on multi-aspect ontologies and smart space-based interaction. To provide the necessary degree of self-organization, a guided self-organization approach is proposed. The proposed human-machine collective intelligence environment can improve decision-making in many complex areas, requiring collective effort and dynamic adaptation to the changing situation.