9 ms·

mind informing what you use Prolog for, and strengths?

deleted 5y ago
[deleted]
YeGoblynQueenne 5y ago
I use Prolog for my research. I study Inductive Logic Programming (ILP) for my PhD. ILP is a field in the intersection of machine learning and logic programming, that studies approaches to learning logic programs from examples, background knowledge and language bias (it helps to think of background knowledge as a library of sub-routines from which a program is to be composed and to think of language bias as constraints on the structure of learned programs). Obviously Prolog is well-suited for this task, but there's a reason why you don't often hear of "Inductive Python Programming" or "Inductive Java Programming", say. The reason is that imperative languages tend to have lots of specialised syntax, for example for class declarations, loops, variable assignment etc. Whereas Prolog syntax consists entirely of one kind of expression, the Horn clause. So for instance, to learn a program with a "loop" in Prolog you "only" need to add a recursive clause to the program, where a recursive clause is simply an ordinary Horn clause with the same predicate symbol in a head literal and one or more body literals. To learn a program with a loop in Python you have to add the loop to the program as a specialised structure with its own peculiar syntax. Also, because in Prolog everything is a Horn clause, examples, background knowledge and language bias can be (and often are) represented as Prolog programs themselves, so it's possible to learn new background knowledge, new language bias and even new examples. That'd be tricky to do in Python where examples, say, would be not programs, but the inputs of and outputs to programs. The sister field to ILP, of Inductive Functional Programming exploits the homoiconicity of functional languages in similar ways. Finally, Prolog is a language with a deductive inference algorithm as an interpreter and it turns out deduction can be sort of inverted into induction. Which is to say, we can go from reasoning to learning, with but a tiny little hop. Well, ish. If you're interested in more details about my work, there's links in my profile.

deleted 5y ago

[deleted]

I use Prolog for my research. I study Inductive Logic Programming (ILP) for my PhD. ILP is a field in the intersection of machine learning and logic programming, that studies approaches to learning logic programs from examples, background knowledge and language bias (it helps to think of background knowledge as a library of sub-routines from which a program is to be composed and to think of language bias as constraints on the structure of learned programs). Obviously Prolog is well-suited for this task, but there's a reason why you don't often hear of "Inductive Python Programming" or "Inductive Java Programming", say. The reason is that imperative languages tend to have lots of specialised syntax, for example for class declarations, loops, variable assignment etc. Whereas Prolog syntax consists entirely of one kind of expression, the Horn clause. So for instance, to learn a program with a "loop" in Prolog you "only" need to add a recursive clause to the program, where a recursive clause is simply an ordinary Horn clause with the same predicate symbol in a head literal and one or more body literals. To learn a program with a loop in Python you have to add the loop to the program as a specialised structure with its own peculiar syntax. Also, because in Prolog everything is a Horn clause, examples, background knowledge and language bias can be (and often are) represented as Prolog programs themselves, so it's possible to learn new background knowledge, new language bias and even new examples. That'd be tricky to do in Python where examples, say, would be not programs, but the inputs of and outputs to programs. The sister field to ILP, of Inductive Functional Programming exploits the homoiconicity of functional languages in similar ways. Finally, Prolog is a language with a deductive inference algorithm as an interpreter and it turns out deduction can be sort of inverted into induction. Which is to say, we can go from reasoning to learning, with but a tiny little hop. Well, ish. If you're interested in more details about my work, there's links in my profile.