4 ms·

Could you provide some papers/link related to Inductive Logic Programming? I'd like to look at other techniques in this space.

YeGoblynQueenne 5y ago
My pleasure! - First, some more recent work, mostly overviews. 1. The following is the most recent overview of the field I'm aware of: Inductive logic programming at 30 (Cropper et al, 2020) https://www.doc.ic.ac.uk/~shm/Papers/ilp30.pdf https://www.doc.ic.ac.uk/~shm/Papers/ilp30.pdf 2. And a slightly shorter version of the same paper that summarises new trends: Turning 30: New Ideas in Inductive Logic Programming (Cropper et al, 2020) https://www.ijcai.org/Proceedings/2020/0673.pdf https://www.ijcai.org/Proceedings/2020/0673.pdf 3. Here's a short introduction to the relatively new ILP direction of learning Answer Set Programming: Inductive Logic Programming in Answer Set Programming (Corapi et al, 2011) https://link.springer.com/chapter/10.1007/978-3-642-31951-8_12 https://link.springer.com/chapter/10.1007/978-3-642-31951-8_... 4. This is an overview of Meta-Interpretive Learning (MIL), a new approach to ILP that overcomes many difficulties of earlier approaches (Full disclosure: my own work is on MIL, though not the article linked): Meta-Interpretive Learning: achievements and challenges (Stephen Muggleton, 2017) https://www.doc.ic.ac.uk/~shm/Papers/rulemlabs.pdf https://www.doc.ic.ac.uk/~shm/Papers/rulemlabs.pdf 5. And this is a (short vesion) of a paper on δILP, a neural-net based ILP system: Learning Explanatory Rules from Noisy Data (Evans and Grefenstette, 2018) https://www.ijcai.org/Proceedings/2018/0792.pdf https://www.ijcai.org/Proceedings/2018/0792.pdf - Next, some earlier work that is still relevant: 6. This is the inaugural paper of the field, that first named it (a little heavy reading though): Inductive Logic Programming (Stephen Muggleton, 1990) https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf 7. Here's an early paper on predicate invention, an important technique in ILP (only recently fully realised via MIL): Predicate Invention in ILP - an Overview (Irene Stahl, 1993) https://link.springer.com/chapter/10.1007%2F3-540-56602-3_144 https://link.springer.com/chapter/10.1007%2F3-540-56602-3_14... 8. And an early overview of learning recursion (and performing predicate invention) that also lists several early ILP systems: Inductive synthesis of recursive logic programs:achievements and prospects (Flener and Yilmaz, 1999) https://core.ac.uk/download/pdf/82810434.pdf https://core.ac.uk/download/pdf/82810434.pdf That should be enough to get you started. I recommend reading in the order I linked to the various articles. I tried to give links to documents that I know can be read for free. Unfortunately most of the material on ILP is either in scholarly articles, or, where there are textbooks, they tend to be older. That sounds bad, but there has been much new work recently with several new approaches. Let me know if you're looking for more specific information. See my signature for contact details- I'm happy to answer emails about ILP :)
- ahgamut 5y ago
  Thank you so much!
- YorkshireSeason 5y ago
  It should be emphasised that inductive programming is not tied to logic programming, and works for every other programming paradigm as well, e.g. functional programming [1, 2]. We could also do IP for imperative programming, although, as far as I am aware, nobody has done this. [1] Feser et al's Lambda-Learner https://www.cs.utexas.edu/~swarat/pubs/pldi15.pdf https://www.cs.utexas.edu/~swarat/pubs/pldi15.pdf [2] S. Katayama's MagicHaskeller http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.html http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.ht...
  - YeGoblynQueenne 5y ago
    That's absolutely true! But the OP asked about ILP in particular. To be fair, logic and functional programming languages do have some advantages as target languages for Inductive Programming compared to imperative languages in that they have very simple syntax. For example, Prolog doesn't even have variable declarations. That's very convenient because the learning system only needs to learn the logic of the program, not the syntax of the language also. It's also much simpler to define language bias or program schemata etc constraints on the form of hypotheses in such languages, or even order programs by generality. For instance, Prolog has unification built-in and unification is used in ILP to order programs by generality (by testing for subsumption). All this machinery would have to be implemented from scratch in an imperative language. Although the reason that logic and functional programming languages are given more weight in IP is probably for historical reasons, because Lisp and Prolog were, for a long time, "the languages of AI". I'm trying to remember... I think there's been some IP work on imperative languages, maybe even Python. I'll need to check my notes.
    - YorkshireSeason 5y ago
      > ILP to order programs by generality Sorry, naive question: does ILP test candidate programs by increasing or decreasing generality?

YeGoblynQueenne 5y ago

My pleasure! - First, some more recent work, mostly overviews. 1. The following is the most recent overview of the field I'm aware of: Inductive logic programming at 30 (Cropper et al, 2020) https://www.doc.ic.ac.uk/~shm/Papers/ilp30.pdf https://www.doc.ic.ac.uk/~shm/Papers/ilp30.pdf 2. And a slightly shorter version of the same paper that summarises new trends: Turning 30: New Ideas in Inductive Logic Programming (Cropper et al, 2020) https://www.ijcai.org/Proceedings/2020/0673.pdf https://www.ijcai.org/Proceedings/2020/0673.pdf 3. Here's a short introduction to the relatively new ILP direction of learning Answer Set Programming: Inductive Logic Programming in Answer Set Programming (Corapi et al, 2011) https://link.springer.com/chapter/10.1007/978-3-642-31951-8_12 https://link.springer.com/chapter/10.1007/978-3-642-31951-8_... 4. This is an overview of Meta-Interpretive Learning (MIL), a new approach to ILP that overcomes many difficulties of earlier approaches (Full disclosure: my own work is on MIL, though not the article linked): Meta-Interpretive Learning: achievements and challenges (Stephen Muggleton, 2017) https://www.doc.ic.ac.uk/~shm/Papers/rulemlabs.pdf https://www.doc.ic.ac.uk/~shm/Papers/rulemlabs.pdf 5. And this is a (short vesion) of a paper on δILP, a neural-net based ILP system: Learning Explanatory Rules from Noisy Data (Evans and Grefenstette, 2018) https://www.ijcai.org/Proceedings/2018/0792.pdf https://www.ijcai.org/Proceedings/2018/0792.pdf - Next, some earlier work that is still relevant: 6. This is the inaugural paper of the field, that first named it (a little heavy reading though): Inductive Logic Programming (Stephen Muggleton, 1990) https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf https://www.doc.ic.ac.uk/~shm/Papers/ilp.pdf 7. Here's an early paper on predicate invention, an important technique in ILP (only recently fully realised via MIL): Predicate Invention in ILP - an Overview (Irene Stahl, 1993) https://link.springer.com/chapter/10.1007%2F3-540-56602-3_144 https://link.springer.com/chapter/10.1007%2F3-540-56602-3_14... 8. And an early overview of learning recursion (and performing predicate invention) that also lists several early ILP systems: Inductive synthesis of recursive logic programs:achievements and prospects (Flener and Yilmaz, 1999) https://core.ac.uk/download/pdf/82810434.pdf https://core.ac.uk/download/pdf/82810434.pdf That should be enough to get you started. I recommend reading in the order I linked to the various articles. I tried to give links to documents that I know can be read for free. Unfortunately most of the material on ILP is either in scholarly articles, or, where there are textbooks, they tend to be older. That sounds bad, but there has been much new work recently with several new approaches. Let me know if you're looking for more specific information. See my signature for contact details- I'm happy to answer emails about ILP :)

ahgamut 5y ago
Thank you so much!
YorkshireSeason 5y ago
It should be emphasised that inductive programming is not tied to logic programming, and works for every other programming paradigm as well, e.g. functional programming [1, 2]. We could also do IP for imperative programming, although, as far as I am aware, nobody has done this. [1] Feser et al's Lambda-Learner https://www.cs.utexas.edu/~swarat/pubs/pldi15.pdf https://www.cs.utexas.edu/~swarat/pubs/pldi15.pdf [2] S. Katayama's MagicHaskeller http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.html http://nautilus.cs.miyazaki-u.ac.jp/~skata/MagicHaskeller.ht...
- YeGoblynQueenne 5y ago
  That's absolutely true! But the OP asked about ILP in particular. To be fair, logic and functional programming languages do have some advantages as target languages for Inductive Programming compared to imperative languages in that they have very simple syntax. For example, Prolog doesn't even have variable declarations. That's very convenient because the learning system only needs to learn the logic of the program, not the syntax of the language also. It's also much simpler to define language bias or program schemata etc constraints on the form of hypotheses in such languages, or even order programs by generality. For instance, Prolog has unification built-in and unification is used in ILP to order programs by generality (by testing for subsumption). All this machinery would have to be implemented from scratch in an imperative language. Although the reason that logic and functional programming languages are given more weight in IP is probably for historical reasons, because Lisp and Prolog were, for a long time, "the languages of AI". I'm trying to remember... I think there's been some IP work on imperative languages, maybe even Python. I'll need to check my notes.
  - YorkshireSeason 5y ago
    > ILP to order programs by generality Sorry, naive question: does ILP test candidate programs by increasing or decreasing generality?