essential reading list?

To clarify  the role of KR, we can take the example of the AI Strategist
Plan being drafted, as per the last meeting

The plan had been started a week before, and was describing some lifecycle
development activities,
by applying logic and reasoning and understanding of the topic - AI KR it
is possible to see that the plan was being devised was completely drifting
from its title
This is what KR ultimately does: ensures that what is being done is what is
being said, and that appropriate concepts and vocabulary are
adopted

By applying KR we can show that   that the plan was drifting from its
initial intention, so that we can adjust our focus accordingly

We called it the AI strategy plan but were starting jotting down the
lifecycle phases (in a rather messy way and using very casual terminology)

I dont think it is necessary to have studied to understand AI, after all it
is possible for children to implement a fairly sophisticated robot these
days using a Mindstorm Lego kit simply following the instructions

However KR has generally evolved as an academic discipline, and the clever
bits of AI are part of  KR

As AI advances, KR is becoming more critical  to ensure systems are what
they say they are and do what they say they do, yet KR is poorly
understood, as Chris and Owen confirm, despite having been on this list for
some time, they still have doubts about what it is about.
This is my research interest and the main motive for setting up the group
as an open forum and possible outlet

So I ll produce an essential reading list on KR for members, and this could
also be a deliverable /outcome of this CG, so that we can reduce the
learning curve for newcomers to the subject and help to make it clear what
is it that we are doing, and why -

Of course if people dont know AI and KR, they may struggle to make
contributions to the core topic of this CG
It would help to understand a bit of Logic - especially non monotonic logic
https://plato.stanford.edu/entries/logic-nonmonotonic/

If people come from engineering background, rather than complex science or
information science it should still be possible to
acknowledte  the less mechanistic (trivial) and more intellectual level of
AI challenges that we are trying to address with our work
This book can be browsed online
https://play.google.com/store/books/details?id=ppX8wf7kkHkC&rdid=book-ppX8wf7kkHkC&rdot=1&source=gbs_vpt_read&pcampaignid=books_booksearch_viewport

it touches upon KR briefly

I ll work on some resources and an essential reading list, and I ll also
work on emphasising the KR related issues for those
who find it difficult to focus on them . It is a rather academic topic and
I am happy to make it more accessible for anyone

This is the role of the learning resources which will be one of the
deliverables of this CG