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.
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.
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.
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?
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.
[1] https://en.wikipedia.org/wiki/Eurisko