Open AccessConceptual Framework of a Human-Machine Collective Intelligence Environment for Decision Support
Author(s) -
Alexander Smirnov,
Andrew Ponomarev,
Tatiana Levashova,
Nikolay Shilov
Publication year - 2022
Publication title -
dokladi na bʺlgarskata akademiâ na naukite
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.244
H-Index - 17
eISSN - 2367-5535
pISSN - 1310-1331
DOI - 10.7546/crabs.2022.01.12
Subject(s) - interoperability , adaptation (eye) , collective intelligence , computer science , ambient intelligence , knowledge management , conceptual framework , group decision making , space (punctuation) , artificial intelligence , psychology , world wide web , sociology , social psychology , social science , neuroscience , operating system
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.