Problem is, OpenCog (and SOAR, too, when I saw it) is still based in a fundamentally certainty-based way of looking at AI tasks, rather than one focused on probability and optimization.

Uh, what were you looking at? The basic foundation of OpenCog is a probabilistic logic called PLN (the wrong one to be using, IMHO, but a probabilistic logic nonetheless). Everything in OpenCog is expressed and reasoned about in probabilities.

To do that it's going to need a decent sense of probability and expected utility. Problem is, OpenCog (and SOAR, too, when I saw it) is still based in a fundamentally certainty-based way of looking at AI tasks, rather than one focused on probability and optimization.

I don't see why this follows. It might be that mildly smart random search, plus a theorem prover with a fixed timeout, plus a benchmark, delivers a steady stream of useful optimizations. The probabilistic reasoning and utility calculation might be implicit in the design of the "self-improvement-finding submodule", rather than an explicit part of the overall architecture. I don't claim this is particularly likely, but neither does undecidability seem like the fundamental limitation here.