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A comment on Neural Architecture Search with Reinforcement Learning. Parent comment

Re:Neural Architecture Search with Reinforcement Learning by randcraw on Thursday January 19, 2017 @05:00PM (#13439848)
Of course compound task federation like this is not exactly new. Blackboard systems did the very same thing using expert systems back in the 1970s-80s.

The problems then remain today: complex and compound tasks don't decompose neatly into well-defined constituent subtasks. Sub-task recombination rapidly devolves into a rat's nest of selecting from competing subtask components that don't have clearly distinct semantics, that are not free of contextual dependencies, nor do they plug-in-and-play independently.

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Re:Neural Architecture Search with Reinforcement Learning by ced on Thursday January 19, 2017 @06:02PM (#13440284)
Do you have any interesting material on this? Where's a good in-depth analysis of blackboard systems' pros and cons?
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Re:Neural Architecture Search with Reinforcement Learning by randcraw on Thursday January 19, 2017 @06:53PM (#13440558)
I'd start here: https://en.wikipedia.org/wiki/Blackboard_system

I'm not up-to-date, but I haven't heard of new work on blackboards or federated rule-based systems for maybe 20 years now, after expert systems grew increasingly probabilistic (and procrustean), and BBSes based on binary rules showed little sign of escaping the classic brittleness of RBSes.

The wiki article mentions 'Bayesian Blackboard' systems. Maybe they had greater success?

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Re:Neural Architecture Search with Reinforcement Learning by joshmarlow on Thursday January 19, 2017 @07:38PM (#13440819)
BBS systems also interest me quite a bit. Check out https://en.wikipedia.org/wiki/LIDA_(cognitive_architecture). It's based on a Global Workspace model (conceptually similar to a Black Board System). You can find papers on LIDA online (including one or two from the references on the wiki page). If you find anything particularly interesting, feel free to share!
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