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From: Mild Shock <janburse@fastmail.fm>
Newsgroups: comp.lang.prolog
Subject: Re: Decrease Entropy (Was: Alain Colmerauer Prolog Systems Price
 (Was: The Heat Death of Prolog)
Date: Thu, 17 Oct 2024 16:41:45 +0200
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Corr.:

So how can we decrease entropy in Prolog
systems design? Here some bad examples
that rather increase entropy:

Mild Shock schrieb:
> So how can we decrease entropy in Prolog
> systems design. Here some examples:
> 
> - Janus nonsense:
>    Nobody cares about features terms, just
>    use member(x-V, [x-10,y-20]) (*), nobody needs
>    a copying foreign function interface.
> 
> - ProB nonsense:
>    Papers like "Making ProB compatible with
>    SWI-Prolog" rater point to problems, than
>    to problem solution pairs.
> 
> (*) Ok, that was sarcasm with a grain of
> salt. But its folk knowledge that for for
> small size dicts linear search is indeed
> on par with binary search.
> 
> Mild Shock schrieb:
>> Hi,
>>
>> The price that nobody needs:
>>
>> - Alain Colmerauer Prolog Heritage Prize
>>    recent practical accomplishments that
>>    highlight the benefits of Prolog-inspired
>>    computing for the future
>>
>>    - Theresa Swift and Carl Andersen
>>      Janus nonsense
>>
>>    - Michael Leuschel and STUPS Group
>>      ProB nonsense
>>
>> https://logicprogramming.org/alain-colmerauer-prize/
>>
>> The price that everybody wants:
>>
>> - Alain Colmerauer Prolog Systems Price
>>    For contributions of lasting and major
>>    technical importance to Prolog Systems
>>    design.
>>
>>    - Mats Carlsson: SICstus Prolog
>>    https://www.ri.se/en/person/mats-carlsson
>>
>>    - Jan Wielemaker: SWI Prolog
>>    https://en.wikipedia.org/wiki/Jan_Wielemaker
>>
>>    - Ulrich Neumerkel: ISO Standard
>>    https://informatics.tuwien.ac.at/people/ulrich-neumerkel
>>
>>    - Markus Triska: CLP Integration
>>    https://www.metalevel.at/
>>
>>    - Taisuke Sato: Tabulated Resolution
>>    https://rjida.meijo-u.ac.jp/sato-www/sato/
>>
>>    Etc.. Etc..
>>
>> Bye
>>
>> Mild Shock schrieb:
>>> An increase in entropy indicates greater uncertainty
>>> in the implementers's choice of operations,
>>> potentially reflecting a decrease of intuition.
>>>
>>> Maximum entropy occurs when all operations are
>>> equally likely, corresponding to a state where
>>> the implementer acts randomly due to lack of
>>>
>>> intuitive guidance. In this framework, we might
>>> interpret the "heat death" of Prolog as a state
>>> where implementers no longer have effective
>>>
>>> intuition or insight to guide their work.
>>>
>>> AI-driven development of Prolog systems
>>> https://lims.ac.uk/documents/undefined-10.pdf
>>
>