Home // AISyS 2025, The Second International Conference on AI-based Systems and Services // View article
Authors:
Steve Chan
Keywords: artificial intelligence systems; machine learning; Lower Ambiguity Higher Uncertainty (LAHU); Higher Ambiguity Lower Uncertainty (HALU); isomorphic engine; domain knowledge communication; multi-criteria decision-making; decision quality; decision engineer
Abstract:
The increasing use of Artificial Intelligence (AI) has led to a myriad of Swarm Intelligence (SI) opportunities, wherein collective learning can occur, such as Machine Learning (ML) on ML, as well as collective Multi-Criteria Decision-Making (MCDM). Effective ML on ML tends to involve Knowledge Transfer (KT) via a Domain Knowledge Communication (DKC) channel, wherein successful interpretation of both the knowledge and the inferential processes involved is central. This is particularly important when temporal considerations matter. The conveyance of concepts, similar to the functioning of a Large Concept Model (LCM), exhibits promise, and various benchmarks — to ensure such a successful conveyance — have been scrutinized. However, while various efforts have been expended on the machine-centric side of the AI System (AIS) divide, a certain Achilles heel may reside on the human-centric side of the overarching Socio-Technical System (STS) in the form of non-concept model-centric Likert-derived information. This paper will progress through some machine-centric side experimental forays and then hone in on the Likert-centric repertoire on the other side of the AIS divide. A mitigation construct is proposed, and preliminary explorations exhibit some promise.
Pages: 15 to 26
Copyright: Copyright (c) IARIA, 2025
Publication date: September 28, 2025
Published in: conference
ISBN: 978-1-68558-303-3
Location: Lisbon, Portugal
Dates: from September 28, 2025 to October 2, 2025