I started using Prolog in my self written home automation system over 20 years ago. At first I was using CORBA and I linked ACE/Tao into SWI-Prolog so that Prolog could catch and send CORBA messages. That worked for years but was too annoying to add new message types since a wrapper had to be written for each, plus threading had to be coordinated between C++ and Prolog. Eventually I ditched the CORBA stuff and switched to MQTT, but instead of binding the C++ and Prolog together I found and extended MQTT support for Prolog directly, actually I've mostly replaced the C++ parts of my HA system with Java. The Prolog is pretty nice the way I can now specify predicates for MQTT topic paths, and I use shared topics for scalability. Now all of this is running deployed in k3s.
> We recently installed Gateway multi-media kits on our PCs, but found the installation less than trivial because of conflicts in our interrupt (IRQ) channels. A simple expert system could have helped to resolve those IRQ conflicts. ... The sample program is set up to allow installation of two different devices, a 'Sound Blaster' and a 'Mitsumi CD- ROM'.
This was a real blast from the past. I wonder why more systems today don't have this kind of logic solving built in. Possibly, too many complex behaviours that are not cleanly quantified.
Pepperidge Farm remembers when reasoning systems could actually reason, instead of backing out the part of the context they're told is erroneous, appending "Wait..." and then continuing to predict tokens as before.
Ironically, once upon a time Prolog and logic programming in general were part of the cutting-edge of AI. There's quite a fascinating history of Japan's fifth-generation computing efforts in the 1980s when Japan focused on logic programming and massively parallel computing. My former manager, who is from Japan, earned his PhD in the 1990s in a topic related to constraint logic programming.
I remember when so-called "expert systems" written in Prolog or LISP were supposed to replace doctors. Then came the (first) AI winter after people realized how unrealistic that was.
Nowadays LLMs are supposed to replace doctors.. and that makes even less sense given that LLMs are error-prone by design. They will hallucinate, you cannot fix that because of their probabilistic nature, yet all the money in the world is thrown at people who preach LLMs will eventually be able to do every human job.
Even now NEC makes some cool massively parallel chips and accelerators that I wish were more mainstream because they look like they'd be fun to play with.
Louise (Patsantzis & Muggleton 2021) is a machine learning system that learns Prolog programs.
Louise is a Meta-Interpretive Learning (MIL) system. MIL (Muggleton et al. 2014), (Muggleton et al. 2015), is a new setting for Inductive Logic Programming (ILP) (Muggleton, 1991). ILP is a form of weakly-supervised machine learning of logic programs from examples of program behaviour (meaning examples of the inputs and outputs of the programs to be learned). Unlike conventional, statistical machine learning algorithms, ILP approaches do not need to see examples of programs to learn new programs and instead rely on background knowledge, a library of pre-existing logic programs that they reuse to compose new programs.
This is what was done by Douglas Lenat from late 1970-s on [1]. He did his work using Lisp, this thing does something close using Prolog.
If we're going down that path: Ehud Shapiro got there back in 1984 [1]. His PhD thesis is excellent and shows what logic programming could do (/could have been).
He viewed the task of learning predicates (programs/relations) as a debugging task. The magic is in a refinement operator that enumerates new programs. The diagnostic part was wildly insightful -- he showed how to operationalise Popper's notion of falsification. There are plenty of more modern accounts of that aspect but sadly the learning part was broadly neglected.
There are more recent probabilistic accounts of this approach to learning from the 1990s.
... and if you want to go all the way back you can dig up Gordon Plotkin's PhD thesis on antiunification from the early 1970s.
FWIW (not much), around the time of that article, I reversed this: I used Arity Prolog for a morphological parsing program, with C calls for the bit fiddling (because I needed 64 bits, and the Prolog I was using only handled 16 bit strings).
I often wonder what a Prolog implemented as an Objective-C like extension to C would look like. Since WAM has proper stack and heap IIRC, it might be possible to plug that in through some region-based memory management on C side. Is there some prior art like this?
I ported from Pascal to C a Lisp interpreter system that had an embedded Prolog in it (that used Lisp syntax) (and wrote a new memory subsystem) in my spare time in College. Later I helped a grad student a little bit with their implementation of a Warren machine (runtime for a Prolog compiler) for it. That’s the only embedded Prolog I’m aware of.
Things have moved on since 1994, not only can you still embed it in C and a load of other languages, you can even run it directly in your browser as there's a WASM port.
There's another updated version of that prolog here along with some links, including an archived article from Microsoft Research on how it was (once upon a time) used in Windows NT network configuration:
https://github.com/opless/small-prolog
This was a real blast from the past. I wonder why more systems today don't have this kind of logic solving built in. Possibly, too many complex behaviours that are not cleanly quantified.
I see three stories already.
Nowadays LLMs are supposed to replace doctors.. and that makes even less sense given that LLMs are error-prone by design. They will hallucinate, you cannot fix that because of their probabilistic nature, yet all the money in the world is thrown at people who preach LLMs will eventually be able to do every human job.
The second AI winter cannot come soon enough.
[1] https://en.wikipedia.org/wiki/Eurisko
He viewed the task of learning predicates (programs/relations) as a debugging task. The magic is in a refinement operator that enumerates new programs. The diagnostic part was wildly insightful -- he showed how to operationalise Popper's notion of falsification. There are plenty of more modern accounts of that aspect but sadly the learning part was broadly neglected.
There are more recent probabilistic accounts of this approach to learning from the 1990s.
... and if you want to go all the way back you can dig up Gordon Plotkin's PhD thesis on antiunification from the early 1970s.
[1] https://en.wikipedia.org/wiki/Algorithmic_program_debugging
In case you weren't aware, people are using Prolog with LLMs;
https://news.ycombinator.com/item?id=42039527
https://news.ycombinator.com/item?id=45712934
https://web.archive.org/web/20030218034509/http://www.resear...
https://www.swi-prolog.org/pldoc/man?section=wasm-version
Disclaimer: I'm no expert on LLMs.