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OP here:

I indeed meant it in the philosophical sense you describe. But I am very interested in the possible technical solutions. I tried to describe (in the chapter "Connecting ML and rules") the approaches I know of, none of which are very exciting.

I'd love to hear if anybody knows of good approaches.

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As part of my graduate work with George Konidaris we've been exploring the creation of symbols and operators with ML. The goal being symbolic planning for continuous systems, however I see similarities in our approach, and the goals of rule based systems.

There's a journal paper that's under development, but here's a conference paper that addresses some of George's early work: http://cs.brown.edu/people/gdk/pubs/sym-prob.pdf

Thanks a lot - will look it up.

maybe your test cases are your rules. as long as you're recording things over time you have data to feed back to and learn from. also, each level of abstraction you could store less data to potential learn from. instead of storing every pixel just store edges and other low level features from the first layer.

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