Conceptual Framework of a Human-Machine Collective Intelligence Environment for Decision Support
Keywords:collective intelligence, human-machine systems, ontologies, semantic interoperability, decision support
The paper extends collective intelligence understanding to the problem-solving 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 re- lies 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.
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LicenseCopyright (c) 2022 Proceedings of the Bulgarian Academy of Sciences
Copyright (c) 2022 Proceedings of the Bulgarian Academy of Sciences
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