Lise Getoor from MIT is also doing a lot of work with statistical relational learning in particular, which is kind of a cross between GOFAI (the "relational" bit) and nouveau AI (the "statistical" part):
I think this is not exactly what you were asking for but I think you might find it interesting, particularly Inductive Logic Programming (of which there's oodles in De Raedt's book).
Personally, I started an MSc in AI because I was interested in all the symbolic stuff, and I was a bit disappointed to find there were almost none of that left in the curriculum (at my school anyway, the University of Sussex). I guess I kind of see the point though- knowledge engineering is pretty hard and costly and learning from data is very attractive. I really don't like the idea of doing it all with statistical black-boxes, though. So relational learning is a kind of halfway house for me, where I can still use mathematical logic principles and not have to eat the statisticians' dust, so to speak. For me relational learning is the spiritual successor of GOFAI that I was looking for, so I think you may find something of interest in the links above.
Edit: there's also the probabilistic logic programming community, forgot about them; here:
That's from the University of Leuven, where De Raedt is from. The name says it all: Horn clauses with probabilistic weighting. So that's not machine learning (or any kind of learning, it's just what it says on the tin). You will find many more here:
http://www.springer.com/us/book/9783540200406
Lise Getoor from MIT is also doing a lot of work with statistical relational learning in particular, which is kind of a cross between GOFAI (the "relational" bit) and nouveau AI (the "statistical" part):
http://www.cs.umd.edu/srl-book/
In the UK I know of Kristian Kersting who does statistical relational learning research, at Dortmund:
http://www-ai.cs.uni-dortmund.de/PERSONAL/kersting.html
I think this is not exactly what you were asking for but I think you might find it interesting, particularly Inductive Logic Programming (of which there's oodles in De Raedt's book).
Personally, I started an MSc in AI because I was interested in all the symbolic stuff, and I was a bit disappointed to find there were almost none of that left in the curriculum (at my school anyway, the University of Sussex). I guess I kind of see the point though- knowledge engineering is pretty hard and costly and learning from data is very attractive. I really don't like the idea of doing it all with statistical black-boxes, though. So relational learning is a kind of halfway house for me, where I can still use mathematical logic principles and not have to eat the statisticians' dust, so to speak. For me relational learning is the spiritual successor of GOFAI that I was looking for, so I think you may find something of interest in the links above.
Edit: there's also the probabilistic logic programming community, forgot about them; here:
https://dtai.cs.kuleuven.be/problog/
That's from the University of Leuven, where De Raedt is from. The name says it all: Horn clauses with probabilistic weighting. So that's not machine learning (or any kind of learning, it's just what it says on the tin). You will find many more here:
http://probabilistic-programming.org/wiki/Home
Although that's not all _logic_ programming (but there's quite a bit of it).