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
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
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
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
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