I've been saying something like that since the 1980s, but less abstractly. I've argued that "common sense" is the ability to examine a proposed course of short term action and predict generally what will happen. I used to work on this at a low level, along the lines of "much of life is about getting through the next 15 seconds in the real world without falling down or bumping into anything". That's needed to survive in the real world. That led me into automatic driving, legged running, grasping by touch, and similar low level problems. Most of the brain in lower level mammals manages things at that level. Once you've got that, maybe some higher level can back-seat drive the short-term system to achieve higher level goals. I argued for getting the lower level right first. AI still isn't very good at this, which is why mobile robots are not yet useful.

It's clear that machine learning as we know it today has real problems doing strong AI. We all know that. But this paper does not demonstrate that the author's pet approach is any better. There are no examples. No working systems. Also, trying to hammer the world into predicate calculus just doesn't work. I went through Stanford at the peak of that idea in the mid-1980s, and watched all the big names hit a wall.