Extending C with Prolog (1994)
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Extending C with Prolog (1994)
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Re: Extending C with Prolog (1994)
#2This 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.
Re: Extending C with Prolog (1994)
#3I see three stories already.
Re: Extending C with Prolog (1994)
#4Is this the time of year when we try to force redditors to stay away by posting about Prolog? I see three stories already.
Re: Extending C with Prolog (1994)
#5Re: Extending C with Prolog (1994)
#6Is this the time of year when we try to force redditors to stay away by posting about Prolog? I see three stories already.
Refreshing stories between all the AI ones (and crypto/web3 before that)
Re: Extending C with Prolog (1994)
#7Is this the time of year when we try to force redditors to stay away by posting about Prolog? I see three stories already.
Re: Extending C with Prolog (1994)
#8Earlier quoted context omitted.
Refreshing stories between all the AI ones (and crypto/web3 before that)
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.
Re: Extending C with Prolog (1994)
#9Is this the time of year when we try to force redditors to stay away by posting about Prolog? I see three stories already.
Refreshing stories between all the AI ones (and crypto/web3 before that)
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.Re: Extending C with Prolog (1994)
#10Is this the time of year when we try to force redditors to stay away by posting about Prolog? I see three stories already.