Name: Logical and Relational Learning
ISBN: 9783642057489

What Hacker News says

YeGoblynQueenneAug 2018

Not rude at all! There are precious few textbooks on ILP, it's mostly a bunch of scattered papers published in various journals and conferences. Nienhuys-Cheng and de Wolf are an excellent first point of call. You can also try the following books:

Logical and Relational Learning - Luc de Raedt [https://www.amazon.com/Logical-Relational-Learning-Cognitive...]

Probabilistic Inductive Logic Programming, Theory and applications - Luc de Raedt, Paolo Frasconi, Kristian Kersting, Stephen Muggleton [https://www.amazon.com/Probabilistic-Inductive-Programming-L...]

And this book chapter (although it's probably nothing new compared to Foundations of ILP):

Inductive Logic Programming: Theory and Methods - Stephen Muggleton and Luc de Raedt https://www.sciencedirect.com/science/article/pii/0743106694...

Unfortunately, there are, as of yet, no textbooks on Meta-Interpretive Learning and I don't expect there to be one for a few years still. MIL is a very new area of research. The first papers were published only in 2015 and we are basically still working on it furiously. It will take a while before there is enough material for a textbook. All the same, I am planning to do a few internet postings regarding MIL, at some point in the near future.

Please feel free to contact me via my profile email, if you would like some pointers to MIL and more general ILP resources other than the ones discussed above.

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YeGoblynQueenneApr 2016

Constraint logic programming is logic programming extended with constraints, and a constraint solver is an engine for checking and resolving such constraints. I guess that's not very useful if you don't know what constraints are but a full explanation can get very involved very quickly, so check out the wikipedia page for a brief (ish) intro.

From personal experience, if you're used to programming in a logic programming language, trying CLP out is like being in declarative heaven. Swi Prolog, as I think all the full Prolog implementations, has a few CLP libraries, frex see:

http://www.swi-prolog.org/pldoc/man?section=summary-lib-clp-...

Minikanren is a relational language that's introduced in the Reasoned Schemer as an example of a relational language. I don't think it's fully a logic programming language (ie, no theorem proving) but I might be wrong. Check out the book for more.

Inductive Logic Programming is a machine learning technique for learning Prolog programs from data. More to the point, you declare knowledge about the world as positive and negative examples given as Prolog unit clauses (facts). An inductive Logic Programming algorithm then will learn a full Prolog program for you, including definite clauses (rules).

A typical example of ILP is FOIL by Quinlan (the Decision Trees guy) [http://www.rulequest.com/Personal/].

ILP is closely er, related to Relational Learning a.k.a. Statistical and Relational Learning and a number of probabilistic logic programming languages like Problog (no typo).

You can find a lot of information about ILP in the textbook Logical and Relational Learning by Luc de Raedt [http://www.springer.com/us/book/9783540200406], which I recommend (not related to the author) (but I am a logic programming looney so).

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myclAug 2018

Sorry to be rude, but could you ask/beg Prof Muggleton to write a book on ILP and/or meta-interpretive learning? Something like an expanded version of his outline webpage (https://www.doc.ic.ac.uk/~shm/ilp_theory.html).

My go-to for trying to understand ILP theory is Nienhuys-Cheng and de Wolf, _Foundations of Inductive Logic Programming_ and De Raedt's _Logical and Relational Learning_ is a more up-to-date overview, but I feel like there's room for a more modern textbook. This new MIL stuff seems to be be an important unifying idea that also connects back to abductive logic programming, if I understand correctly.

Just a request; no offence intended!

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