How can machine learning help extract a Boolean relationship in a given binary input-output data set?
Let us assume that the given data set is exhaustive - ie. it cover all possible input combinations, can we derive the boolean relationship between inputs and outputs? (Say we assume x inputs and 1 output)
Further, if the dataset is not exhaustive, can we predict the output for "(n+1)"th input vector if we know outputs for the previous "n" input vectors? (in other words, can we "learn" how the function behaves?)
I'd greatly appreciate if you can share what approach should I devise to work on this problem, and if possible, about any on-going research on this topic?
UPDATE: As rightly pointed out by @D.W. Several heuristics exist to tackle such NP-hard problems in logic synthesis and minimization.
The essence of my question is to find out if any Machine Learning algorithm can be adopted and developed, which potentially performs better than the currently known heuristics when deployed at scale.
And importantly, if any "recent" advances in ML can be of help in developing tools for Electronic Design Automation?