Autonomous Human-Machine Teams: Knowledge, Information, and Information Gaps in Knowledge

A special issue of Knowledge (ISSN 2673-9585).

Deadline for manuscript submissions: 15 February 2025 | Viewed by 1580

Special Issue Editor


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Guest Editor
Department of Mathematics and Psychology, Paine College, Augusta, GA 30901, USA
Interests: autonomous human-machine teams and systems (A-HMT-S); artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the past, we have organized Special Issues for AI Magazine (2019);  Frontiers in Physics (2023); and Entropy (ongoing, until August, 2024).  Recently, we were offered an opportunity to organize a new Special Interest proposal for the journal Knowledge, which follows in draft

If you agree to publish one manuscript in Knowledge and collect one other author to submit a manuscript in Knowledge, you will become a co-editor along with me, in the order of confirmation(s).

This Special Issue calls for submissions to Knowledge that address what can be knowable about autonomous human-machine teams. We include propositional knowledge (a declarative assertion that a claim about reality is true); procedural knowledge (an assertion that a claim is true about how best to perform a task in reality; e.g., engineering decisions, applications, data management); and the unknowable knowledge about interactions that is sui generis to teams, especially those affected by interdependence, as occurs in all human-human interactions. We claim that interactions can cause a gap in knowledge to be unknowable (Lawless et al., 2023).

Dr. William Lawless
Guest Editor

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Keywords

  • propositional knowledge
  • procedural knowledge
  • unknowable knowledge (embodied cognition)
  • Shannon information
  • non-decomposable interaction
  • interdependence
  • autonomy

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Published Papers (1 paper)

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Research

27 pages, 475 KiB  
Article
Shannon Holes, Black Holes, and Knowledge: The Essential Tension for Autonomous Human–Machine Teams Facing Uncertainty
by William Lawless and Ira S. Moskowitz
Knowledge 2024, 4(3), 331-357; https://doi.org/10.3390/knowledge4030019 - 5 Jul 2024
Viewed by 1076
Abstract
We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as [...] Read more.
We develop a new theory of knowledge with mathematics and a broad-based series of case studies to seek a better understanding of what constitutes knowledge in the field and its value for autonomous human–machine teams facing uncertainty in the open. Like humans, as teammates, artificial intelligence (AI) machines must be able to determine what constitutes the usable knowledge that contributes to a team’s success when facing uncertainty in the field (e.g., testing “knowledge” in the field with debate; identifying new knowledge; using knowledge to innovate), its failure (e.g., troubleshooting; identifying weaknesses; discovering vulnerabilities; exploitation using deception), and feeding the results back to users and society. It matters not whether a debate is public, private, or unexpressed by an individual human or machine agent acting alone; regardless, in this exploration, we speculate that only a transparent process advances the science of autonomous human–machine teams, assists in interpretable machine learning, and allows a free people and their machines to co-evolve. The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric. We conclude that the structure of “knowledge”, once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its useful applications are multifaceted (akin to maximum entropy production). Our novel finding is the existence of Shannon holes that are gaps in knowledge, a surprising “discovery” to only find Shannon there first. Full article
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