In section 13.2 Other Multilayer Machines (pp. 231-232) of the book Perceptrons: An Introduction to Computational Geometry (expanded edition, third printing, 1988) Minsky and Papert actually talk about their knowledge of or opinions about the capabilities of what they call the multilayered machines (i.e. perceptrons with many layers or MLPs).
Have you considered "perceptrons" with many layers?
Well, we have considered Gamba machines, which could be described as "two layers of perceptron". We have not found (by thinking or by studying the literature) any other really interesting class of multilayered machine, at least none whose principles seem to have a significant relation to those of the perceptron. To see the force of this qualification it is worth pondering the fact, trivial in itself, that a universal computer could be built entirely out of linear threshold modules. This does not in any sense reduce the theory of computation and programming to the theory of perceptrons. Some philosophers might like to express the relevant general principle by saying that the computer is so much more than the sum of its parts that the computer scientist can afiord to ignore the nature of the components and consider only their connectivity. More concretely, we would call the student's attention to the following considerations:
Multilayer machines with loops clearly open all the questions of the general theory of automata.
A system with no loops but with an order restriction at each layer can compute only predicates of finite order.
On the other hand, if there is no restriction except for the absence of loops, the monster of vacuous generality once more raises its head.
The problem of extension is not merely technical. It is also strategic. The perceptron has shown itself worthy of study despite (and even because of!) its severe limitations, It has many features to attract attention: its linearity; its intriguing learning theorem; its clear paradigmatic simplicity as a kind of parallel computation. There is no reason to suppose that any of these virtues carry over to the many-layered version. Nevertheless, we consider it to be an important research problem to elucidate (or reject) our intuitive judgment that the extension is sterile. Perhaps some powerful convergence theorem will be discovered, or some profound reason for the failure to produce an interesting
"learning theorem" for the multilayered machine will be found.
So, let me address your first question directly.
Backprop wasn't known at the time, but did they know about manually building multilayer perceptrons?
Yes. They say that Gamba machines could be described as a 2-layer perceptron. For reproducibility, here's the definition of the Gamba machine (section 13.1 Gamba Perceptrons and other Multilayer Linear Machines)
\begin{align}
\psi
&=
\left[\sum_{i} \alpha_{i}\left[\sum_{j} \beta_{i j} x_{j}>\theta_{i}\right]>\theta\right] \\
&=
\left[\sum_{i} \alpha_{i} \varphi_{i} >\theta \right]
\end{align}
See also sections 12.4.4. Layer-Machines.
So, let's now address your second question.
Did Minsky & Papert know that multilayer perceptrons could solve XOR at the time they wrote the book, albeit not knowing how to train it?
So, according to the first excerpt, their intuition was the virtues of perceptrons would not carry over to MLPs, but they acknowledge that more research was needed to reject or support this hypothesis.
However, in section 13.0 Introduction of the same book, they write
We believe (but cannot prove) that the deeper limitations extend also to the variant of the perceptron proposed by A. Gamba.
So, they believed that the Gamba machine would not have been able to solve the XOR problem.
However, in the first excerpt, they say that a Turing machine could be built entirely out of linear threshold modules, which seems to be inconsistent with the second excerpt, but that's not really the case because they are not saying how to build a Turing machine out of the linear threshold modules but that just the specific Gamba machine would have the same limitations of the perceptron.
