Re: Towards adopting stratml for the AIKRCG 'strategy': Meeting Report

a KRID focused goal is Create a Core Ontology that clarifies
role/use/properties of KRID within context of 'goals' mapped in StratMl

Towards that goal please peruse :
Core Software Ontology Core Ontology of Software Components Core Ontology
of Services which references Ontology of Goals
http://km.aifb.kit.edu/sites/cos/

An Ontology to aid the Goal-oriented Requirements Elicitation and
Specification for Self-Adaptive Systems
https://www.researchgate.net/publication/221270123_GOORE_Goal-Oriented_and_Ontology_Driven_Requirements_Elicitation_Method

Carl



It was a pleasure to clarify


On Mon, May 18, 2020 at 7:52 PM Paola Di Maio <paoladimaio10@gmail.com>
wrote:

> Thanks Carl for clarifying
>
> what about setting the goal for clarifying /sketch out KRID so that we
> can have a discussion
> I plan to put my hands on the plan in the stratnavapp soon
> P
>
> On Thu, May 14, 2020 at 10:09 PM carl mattocks <carlmattocks@gmail.com>
> wrote:
>
>>  Towards adopting stratml for the AIKRCG 'strategy' ...
>> Given we are AIKR ... we understand that Kairos  signifies a proper or
>> opportune time for action  and our usage of StratMl to EXPLAIN makes us
>> interested in Knowledge-directed Artificial Intelligence Reasoning Over
>> Schemas (KAIROS) DARPA-SN-19-19 .
>>
>> Our discussions have focused on:
>> StratML is our Schema start point  for reasoning, as in, the performance
>> of AIKR   inference
>> <https://www.merriam-webster.com/dictionary/inferences>s is scoped /
>> weighed by the declared strategy.
>> AIKR reasoning uses KRID identifiers and data (aka metadata) properties,
>> such as KR TYPE.
>> KR Types include Declarative and Imperative  (aka procedural).
>>
>>  Carl
>>
>> Chair AIKRCG
>> It was a pleasure to clarify
>>
>>
>> On Thu, May 14, 2020 at 7:37 AM Paola Di Maio <paola.dimaio@gmail.com>
>> wrote:
>>
>>> It is under Owen and Chris's leadership that we are making some progress
>>> towards
>>> adopting stratml for the AIKRCG 'strategy'
>>>
>>> In sum. what are we doing/planning to do as a group is going to be
>>> documented in the plan. and although we are still working  things out, as
>>> we do have moments of brilliance and outbursts of productivity we can put
>>> them down in this stratml plan on the stratnav app so that they ll be a
>>> record of that. should be useful. I apologize again for being very tired
>>> but 9 pm is very late for me. especially when I have had a full day incl
>>> other calls etc-
>>>
>>> a few notes below
>>>
>>>
>>> the plan being developed here
>>>> <https://www.stratnavapp.com/StratML/Part1/413d648b-bd36-418d-af74-e15b0cd8281d/Styled>.
>>>> if anyone is inspired to chip in pls ask editing pass to Chris on this list
>>>>
>>>
>>>
>>>> With reference to our Frameworks goal
>>>> <https://www.stratnavapp.com/StratML/Part1/413d648b-bd36-418d-af74-e15b0cd8281d/Styled#Goal_f1a62bb5-9910-4052-946a-344c0e22272f>,
>>>> I will endeavor to render in StratML Part 2 format any frameworks that may
>>>> be discovered and available on the Web.  Please apprise me of any of which
>>>> you are aware.
>>>>
>>> To clarify -  Jorge aske whether we are using any framework of reference
>>> for our work. which loosely attempts  to study explainability for machine
>>> learning. That particular goal for our CG may need to be refined a little -
>>> I dont think a frameworks exists as such (strategies, methods) but there is
>>> interesting work being done, which I dont think is a framework yet. rather
>>> a compilation of possible techniques. the effectiveness of which may need
>>> to be evaluated in the field. So to answer the question, methods to address
>>> explainability of ML exist but
>>> a) I dont think are frameworks/strategies - this may be our goal? to
>>> gather what is in the field and make a framework?
>>> b) evaluation criteria for the effectiveness of these methods may not
>>> yet be studied, again
>>> could this be our work? I am doing some research in this direction but
>>> not yet conclusive
>>>  I volunteered to take up this task and shall soon update the plan with
>>> some links but  I am putting together a presentation-
>>>  anyone want to contribute?
>>>
>>> The caveat is that  statistical pronability and non parametric methods
>>> in ML are  unpredictable by definition
>>> https://machinelearningmastery.com/uncertainty-in-machine-learning/
>>> http://mlg.eng.cam.ac.uk/zoubin/talks/mit12csail.pdf
>>>
>>> (this is not my field at all, does anyone care to expand?)
>>> so I am not sure how to address this unpredictability other than with
>>> the question
>>>
>>> Can we use known symbolilc KR to  explain ML?
>>>
>>> In the meantime, this Google site-specific query
>>>> <https://www.google.com/search?ie=UTF-8&oe=UTF-8&q=AI+framework&btnG=Google+Search&domains=stratml.us&sitesearch=stratml.us>
>>>> of the StratML collection turns up about 29 hits on the terms "AI
>>>> framework".  Here's <https://www.modzy.com/platform-and-marketplace/>
>>>> the top paid ad-placed hit (not yet in the StratML collection but soon to
>>>> be).
>>>>
>>> thanks -  how do we query for ML explainability framework (a bit more
>>> precise semantically in relation to what we are doing here)
>>>
>>> KRID -  Carl is putting forward a category/concept/type  whereby KR is
>>> identified
>>> so KRID = some value to describe KR identity
>>> Carl started by suggesting the top level distinction for this concept
>>> would be
>>> declarative/procedural
>>> i  do not yet have an opinion about this, but would request Carl to
>>> start sketching out
>>> the taxonomy for KRID as he envisions it. so that we can have a
>>> discussion about it
>>> One considertation is: to what extent is declarative/procedural
>>> knowledge relevant to support ML?  or is KRID intended for AI in general
>>> (not ML) . Carl perhaps you should create this as a goal for yourself.Also
>>> could you clarify the relation of KRID to KAIROS?
>>>
>>> Thanks!
>>>
>>> PDM
>>>
>>