Computing Tricky Probabilities Using Model Counting (opens in new tab)

Complicated causal chains can be tricky to work with, but estimating the probability of a bad event (e.g. nuclear facility meltdown) is extremely important. This article showcases how complicated, inter-dependent causal chains can be analyzed through state-of-the-art propositional model counters.

"-H1 or -X or -Y = True

The highlighted lines are what’s call the Tseitin transformation, which basically means that when H1 is FALSE, (NOT X or NOT y) must be TRUE"

Forgive me if I'm wrong, but I think it should be "when H1 is TRUE, (NOT X or NOT y) must be TRUE" since this is equivalent to "H1 -> NOT (X & Y)"