probabilistic degrees of truth?

Just as a side note, in our AI KR Vocabulary effort, we initially identify
the key concepts and terms
that enable a system to function logically and correctly - something that
seems to be totally missing in AI standards. As discussed Such as for
example misrepresentation, truth maintenance, or integrity  in all of their
forms  and characterised  in sufficient detail with examples and use cases.
I
n the future, the core set vocabulary can be expanded to cover the AI KR
domain in its vastity, but I expect the outcome and effort will depend on
the method adopted (there are many ways that the AI KR can be shaped and
modelled, as we know fully well)
On the subject of Truth alone, there is too much to figure out
here is an interesting/important open access reader for the weekend

Degrees of truthlikeness, independence, equality and order in probabilistic
propositional knowledge representation
https://academic.oup.com/logcom/advance-article/doi/10.1093/logcom/exae031/7724484
robabilistic propositional knowledge representation, as here conceived,
deals with knowledge representation in the form of probabilistic
(p-)valuations of the atomic propositions of a propositional language, of
probability (p-)distributions over propositional state descriptions and
with their relation. The aim of the paper is conceptual: introducing and
illustrating a number of related, normalized concepts regarding both
valuations and distributions: degrees of truthlikeness, (internal)
independence, equality and order. The backbone of these concepts is the
relevant sum of absolute distances. All degrees are in the unit interval
and will be illustrated at the relevant place by data about the
co-morbidity of psychiatric syndromes (Van Loo *et al.*, 2016). In a new
section we illustrate all concepts by an example of weather conditions, to
be precise: (not) windy, (not) hot, (not) rainy days in March 2023, in De
Bilt (NL)
It is a long, deep read, perhaps we can take away a few key concepts for
our vocab

add to vocab: Probabilistic propositional knowledge representation