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I've been looking to learn more about non-machine learning AI research for the past couple of months (essentially, something that is the spiritual successor to

by NhanH 10y ago

I've been looking to learn more about non-machine learning AI research for the past couple of months (essentially, something that is the spiritual successor to the GOFAI/expert system path instead of the currently popular statistical machine learning path). For some reasons it's quite hard. I'd appreciate if anyone can point me to the state of the research in those areas? (Conferences, research groups' names etc).

sbw1 10y ago
Classic non-ML AI research these days sometimes tends to be baked into the background of systems where deep learning is the star-- e.g., all of the search techniques used in AlphaGo are interesting in their own right, but aren't really where the innovation is there. There are researchers actively looking at more holistic AI systems that aren't just the core learning part (often called cognitive architectures). For instance, take a look at Soar: http://soar.eecs.umich.edu/ http://soar.eecs.umich.edu/ It under active development, and has been since the 80s. Soar is very much a spiritual successor to GOFAI (or maybe still is GOFAI), but does incorporate statistical machine learning-- much of the recent work has been around integrating reinforcement learning with symbolic decision-making. ACT-R is another cognitive architecture under active development, but is more used for cognitive modeling (i.e. psychology research): http://act-r.psy.cmu.edu/ http://act-r.psy.cmu.edu/ You can find other related systems by searching for "cognitive architecture", but it is unfortunately a field that attracts a lot of proposals/'designs' for things that never get properly implemented (the above being two notable exceptions).
YeGoblynQueenne 10y ago
If you're looking for symbolic approaches within machine learning, Luc De Raedt's textbook has the works: http://www.springer.com/us/book/9783540200406 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/ 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 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/ 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 http://probabilistic-programming.org/wiki/Home Although that's not all _logic_ programming (but there's quite a bit of it).

by NhanH 10y ago

sbw1 10y ago

Classic non-ML AI research these days sometimes tends to be baked into the background of systems where deep learning is the star-- e.g., all of the search techniques used in AlphaGo are interesting in their own right, but aren't really where the innovation is there. There are researchers actively looking at more holistic AI systems that aren't just the core learning part (often called cognitive architectures). For instance, take a look at Soar: http://soar.eecs.umich.edu/ http://soar.eecs.umich.edu/ It under active development, and has been since the 80s. Soar is very much a spiritual successor to GOFAI (or maybe still is GOFAI), but does incorporate statistical machine learning-- much of the recent work has been around integrating reinforcement learning with symbolic decision-making. ACT-R is another cognitive architecture under active development, but is more used for cognitive modeling (i.e. psychology research): http://act-r.psy.cmu.edu/ http://act-r.psy.cmu.edu/ You can find other related systems by searching for "cognitive architecture", but it is unfortunately a field that attracts a lot of proposals/'designs' for things that never get properly implemented (the above being two notable exceptions).

YeGoblynQueenne 10y ago

If you're looking for symbolic approaches within machine learning, Luc De Raedt's textbook has the works: http://www.springer.com/us/book/9783540200406 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/ 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 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/ 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 http://probabilistic-programming.org/wiki/Home Although that's not all _logic_ programming (but there's quite a bit of it).