Re: definitions, problem spaces, methods

Thanks for putting things in a framework.
Let's start at the beginning. The official term that best describes what we are trying to do is knowledge representation and reasoning for AI.

See:
https://en.wikipedia.org/wiki/Knowledge_representation_and_reasoning
Here the name Brachman, mentioned by Paola pops up.He defines the core issues for knowledge representation: 
   
   - Primitives
   - Meta-representation
   - Incompleteness
   - Definitions and universals vs. facts and defaults
   - Non-monotonic reasoning
   - Expressive adequacy
   - Reasoning efficiency

It is important to note that the issues of primitives, meta-representation and definitions and universals vs. facts and defaults are actually topics central to philosophy, psychology and more recently cognitive science and neuro science.
They center on what is reality and how do we observe and perceive this. Here we deal with consciousness, mind and cognition and the constructs we use, being concepts and percepts in decision-making and reasoning.
Because AI is being used in the physical sciences, medicine, bio and life sciences, humanities, engineering and in the practice of science itself, we must add to this list of Brachman the following issues:   
   - uncertainty principles for measurement in quantum mechanics, signal processing and information theory
   - bias in perception, decision-making and reasoning
   - reality, the nature of reality itself and its relationship to the mind (reasoning), and the codified forms and rules of human thought and reasoning: logic, available since antiquity; dialectics as a process of logical reasoning; and semiotics which focuses on the epistemological properties of the extant domain (see: Philosophy in Reality: Scientific Discovery and Logical Recovery : https://www.mdpi.com/2409-9287/4/2/22)   

   - universal conceptual frameworks and interchangeable conceptualizations using mathematical tools such as category theory, (algebraic) topology etc.   

 
I have left out quantum physics and its application in quantum computing and the recent findings detailing quantum processes at play in the physical brain and how these might relate to processes underlying decision-making and reasoning.
Also left out explicitly is the adequacy of natural language for conceptualization in knowledge representation and reasoning as it is implicitly covered in the core issues pointed out by Brachman.

I think I have covered all the bases that need addressing for knowledge representation and reasoning.
From this and previous posts in numerous discussions we should now be able to come up with an actionable list of general objectives and goals that we may define as broad or as narrow as we deem fit for what this AIKR CG wants to achieve, that we can format using StratML.

Any industry or academic discipline specific application can be found by appropriate scope definition for selection of specific KR&R conceptual frameworks, methods and technologies.

Milton Ponson
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Project Paradigm: Bringing the ICT tools for sustainable development to all stakeholders worldwide through collaborative research on applied mathematics, advanced modeling, software and standards development 

    On Friday, November 4, 2022 at 07:51:26 PM AST, Paola Di Maio <paola.dimaio@gmail.com> wrote:  
 
 Dave asks for definitions, Adeel asks why is this CG called AI KR CGMilton now wants to see KR bible
I ll comment quickly and take this opportunity to create a new diagram 
since answering these questions can serve as reorientation also for folks who joined recently

1. Dave, definitions for KR are to be found in the many textbooks and papers, some of which are linked to our pages (need to reorganise pointers to resources). KR is an established field. I cannot answer all your personal questions that relate to the field. if you study the books,(take my course?) you'll find your answers. most graduate courses do not contain answers to new research questions, this is why we become researchers
You need to study the references and pick a definition that fits what you are trying to do. The example you pointed to yesterday (some ML thatidentifies deepfakes) uses KR, thanks for bringing it up. Why dont you dig up the code and analyze and look into how this NN uses KR to solve the deepfake problem? That would be very interesting-

2.Adeel, this CG is called AI KR because it addresses the problems caused by lack of KR in ML. It was started because some years ago, AI went mad with NN/ML without KR and I started getting nighmares of a world run by unintelligent machines (you should have seen the papers in top conferences, I was in agony)  This CG pointed out that KR needs to be in the picture to make AI intelligence We have succeeded. KR is now back in the AI/ML learning pictureThe topics we have discussed here are being picked up by researchers (yes, plagiarized and published under their own name) Universities have started looking for PhD students in the field of KR applied to AI/MLI am glad something has been achieved.
 There are gaps to be filled, contributions to be made

3. Milton, the KR bible/s  have been written decades ago, then forgottenWe may be able to add future chapters, KR now needs to be understood and practiced in the context of these powerful new technologies coming out from labs. NN coupled with KR (hybrid) are the way forwardThis is what people have been working on thirty years ago alreadyYour idea to start relating the work of this CG to knowledge representation challenges is good, maybe we can find some use cases in other CGs (I am a member of a couple of other CGs and have shared on the list a few posts where the problems they were trying to address are clearly related to KR that we could look at
PDM