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Abstraction refinement (CEGAR) etc.. [Community Blind Spot] (Was: How to not be Artificial Intelligent)

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Hi,

Something tells me the Prolog community has a
sever blind spot, in their Logic education.
Possibly never touch a book like this here,

even not with tweezers:

Undergraduate Texts in Mathematics - 1983
H .- D. Ebbinghaus et. al - Mathematical Logic
http://www.fuchs-braun.com/media/ca80d9e55f6d3bfaffff8005fffffff0.pdf

The front cover features a smiling face,
illustrating Ehrenfeucht Fraisse (EF) games.
There is a compelling relationship between

EF and Fuzzy Testing. Just take A and B, a formal
form of a spec and of some code. This is quite
different from Lorentz Games, where the initial

set-up is different. But here if Anna plays
Player II in G(M1,M2) and Bert plays Player II
in G(M2,M1). Then if Anna has a winning strategy,

then Bert has a winning strategy. Sounds like
Bisimulation again. One of the biggest struggels
for Boris the Loris and Nazi Retart Julio of

all time. Or this complete blunder, navigating
in the dark, trying to identify an elephant:

@kuniaki.mukai
https://swi-prolog.discourse.group/t/cyclic-terms-unification-x-f-f-x-x-y-f-y-f-y-x-y/9097/72

Bye

P.S.: Mostlikely the cardinal sin of the Prolog
Community is that they don't apply Proof Theoretic
methods and Model Theoretic methods on equal

footing. They don't understand how the two
methods are related, even on the most basic
level, such as counter models, which is a level

more basic than EF games. One of the future
challenges for the community could be extending
proof theoretic methods and model theoretic

methods to (seemingly) higher order logic. This
could be quite messy, or not? I am currengly
fascinated by Feferman Operative Sets and

like Melvin Fittings work in higher order logic.

Mild Shock schrieb:
> Hi,
> 
> How it started, some useless GOFAI framing and
> production systems lore:
> 
> Computational Logic and Human Thinking:
> How to Be Artificially Intelligent
> https://www.cambridge.org/core/books/computational-logic-and-human-thinking/C2AFB0483D922944067DBC76FFFEB295 
> 
> 
> How its going, please note CodeMender from Google:
> 
> New Google Riftrunner AI (Gemini 3) Shocks Everyone
> https://www.youtube.com/watch?v=F_YWQ12qQ8M
> 
> Especially note the section about CodeMender(*), and AI
> built on Gemini, which does inspect and suggest changes
> to OpenSource projects.
> 
> So whats the rule of predicting the future in AI. Well
> just take skeptics, like Boris the Loris (**) (nah we don't
> use Fuzzy Testing here, CodeMender uses this among other
> 
> methods), Linus Torwald (nah, AI for OpenSource is still
> far away, CodeMender is here) etc.. Negate what they are
> saying and you get a perfect prediction for 2025 / 2026.
> 
> LoL
> 
> Bye
> 
> (*) Already *old* anouncement from October 6, 2025:
> 
> Introducing CodeMender: an AI agent for code security
> https://deepmind.google/blog/introducing-codemender-an-ai-agent-for-code-security/ 
> 
> 
> (**) Ok, when you don't find Boris the Loris on
> SWI-Prolog discourse, you might find him here:
> 
> Hello. My name is Boris and this is my family. We're
> lorises and we are primates - a bit like small
> monkeys. We tend to move quite slowly which is
> why we are Slow Lorises. We have big eyes so we
> can see well in the dark to catch insects for our dinner.
> 
> My name... is Boris
> https://x.com/mrborisloris
> 
> Mild Shock schrieb:
>> Hi,
>>
>> Descartes’ “divide problems into parts” works
>> only for well-behaved, linear, decomposable systems.
>> But its just that parts might end up as Schrödingers
>>
>> equation. It could be that stable diffusion is the
>> new constraint solver. In a sense, stable diffusion
>> models (or other generative AI) are functioning as
>>
>> probabilistic, fuzzy constraint solvers — but in a
>> very different paradigm from classical logic or
>> formal methods. But what was neglected?
>>
>> - Cybernetics (1940s–50s)
>> Focused on feedback loops, control, and self-regulation
>> in machines and biological systems. Showed that
>> decomposition can fail because subparts are interdependent.
>>
>> - Chaos Theory (1960s–80s)
>> Nonlinear deterministic systems can produce unpredictable,
>> sensitive dependence on initial conditions. Decomposition
>> into parts is tricky: small errors explode, and “solving
>> subparts” may not help predict the whole.
>>
>> - Santa Fe Institute & Complex Systems (1980s–present)
>> Studied emergent behavior, networks, adaptation,
>> self-organization. Linear, reductionist thinking fails
>> to capture dynamics of economic, social, and ecological systems.
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> Hi,
>>>
>>> How it started:
>>>
>>> https://conceptbase.sourceforge.net/
>>>
>>> How its going:
>>>
>>> https://www.ibm.com/products/datastax
>>>
>>> The problem with claims such as " Formal languages,
>>> such as KAOS, are based on predicate logic and
>>> capture additional details about an application
>>> in a precise manner. They also provide a foundation
>>> for reasoning with information models." is that
>>> every thing in the quoted sentence is wrong.
>>>
>>> Real AI systems scale by approximation,
>>> vectorization, distributed representations,
>>> and partial knowledge — not by globally
>>> consistent logical models. No classical requirements
>>> language or ontology captures the informal
>>> cognitive machinery that makes
>>> intelligence flexible. Intelligence needs the
>>> whole messy cognitive spectrum.
>>>
>>> Somehow DataStax looks like n8n married AI embedding.
>>> I hope Amazon, Meta, Google, etc.. get the message.
>>> I don't worry about Microsoft, they might come with
>>>
>>> something from their Encarta corner and Copilot+ is
>>> more Local AI. After all we need things like Wikidata
>>> in a Robot and not in a Data Center.
>>>
>>> LoL
>>>
>>> Bye
>>
> 

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