For part of a paper I am writing on Clinical Decision Support Systems (computer-aided medical decision making, e.g. diagnosis, treatment), I am trying to compare Expert Systems with systems based on Machine Learning approaches (Deep Learning, Artificial Neural Networks, etc.).
Specifically, I am currently trying to make a general comparison (if possible) of expert systems with machine learning systems across dimensions of efficiency and complexity, i.e.
- time complexity
- space complexity
My current line of thinking, after having tried to find literature with limited success, is that, in the case where one is trying to answer questions in a very specific, limited, domain that only requires a few rules (for an expert system), expert systems are relatively "cheap" in terms of these three criteria. However, when a problem/domain becomes more complex, expert systems seem to suffer from the fact that the number of rules needed "explodes", which, I would think, could lead to things such as large search trees or other problems. My feeling from what I have generally read about machine learning approaches is that these adapt better to more complex problems with higher dimensionalities.
I would like to find some information that either confirms/backs up my general impression, or guides me to some other understanding of this.
Unfortunately, I can't seem to find any sources that specifically deal with this kind of comparison. I'm not sure if I this is because my problem statement is to wide/vague, I am not searching correctly, there just isn't much literature, or my question doesn't make sense.
Some of the sources I did manage to find are:
Expert systems are still used and important in areas such as robotics and monitoring. However, the complexity of advanced rules systems can lead to performance issues. ANNs are currently managing to overcome such performance issues through scale-out.
Unfortunately, this is the most explicit source I've found. However, it doesn't really provide any details on which this claim could backed up, nor would I consider this a solid source, especially not in an academic setting.
Checking for the logical consistency of a set of interrelated logical rules results in the formulation of a satisfiability (SAT) problem [Bezem, 1988]. If one assumes only binary variables, say n of them, then the corresponding search space is of size 2n . That is, it can become very large quickly. This is an NP-complete problem very susceptible to the “dimensionality curse” problem [Hansen and Jaumard, 1990]
This mentions "dimensionality curse", but in the context of checking for logical consistency of the rules of an expert system, and not really in the context of run-time-efficiency & complexity.
I have found numerous other articles comparing expert systems and machine learning approaches, e.g. Ravuri et al., 2019, Learning from the experts: From expert systems to machine-learned diagnosis models, but none of them, from what I have seen, compare expert systems and machine learning approaches across the dimensions I am interested in.
Would anyone be able to provide some input on what would be aspects in comparing expert systems and machine learning approaches in terms of the efficiency and complexity criteria listed above, and/or, be able to point me in the right direction?