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Thanks for the reply. Your article caught my attention because I was thinking about the problem of
integrating probabilistic machine learning models with deterministic rule bases
(specifically, first-order logic ones). The rules
themselves would be learned from data, with ILP. I'm starting a PhD on ILP in October and this is
one of the subjects I'm considering (although the choice is not only mine and
I'm not sure if there's enough "meat" in that problem for a full PhD). My intuition is that in the end, the only way to get, like you say, a "natural"
integration between rules and a typical black-box, statistical machine learning
model is to train the model (ANN, or what have you) to interact directly with a
rule-base- perhaps to perform rule selection, or even to generate new rules
(bloody hard), or modify existing ones (still hard). In other words, the rule
base would control the AV, but the ANN would control the rule-base. I think there's gotta be some prior work on this but I haven't even looked yet.
I'm kind of working on it, but not from the point of view of AVs and I'm using
logistic regression rather than ANNs (because it's much simpler to use quickly
and it outputs probabilities). And I'm only "kind of" working on it. And I don't
think it'll come to anything. But, hey, thanks for the inspiration :) |