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Computing Tricky Probabilities Using Model Counting
- zero_k 2y agoComplicated 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.
- tucnak 2y agoBad, bad AI.
- yorwba 2y agoPretty sure it's just the author giving a summary of their work.
- tucnak 2y ago> can be tricky > extremely important > this article showcases and so on...
- dambi0 2y agoClaiming thst the phrase “extremely important” is indicative of AI authorship is preposterous.
- tucnak 2y agoNobody ever made that claim
- zero_k 2y agoHahah yes. Thanks for defending me ;) Apparently making something accessible to the public is AI. I thought it was science communication :D It's what we are all supposed to do as scientists, but somehow nobody pays us to do. So I do it in my free time. And some people thinks it's AI. Weird times.
- zero_k 2y agoHahha you must be kidding. I am the author. And it was not written with AI. I write that blog, and never used AI to make the blog, or to post here. Weird world we are living in... Ghosts, people are seeing ghosts. We should have a beer and then you'll see I'm real. Also, look at my history :)
- oinweo 2y ago"-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)"
- zero_k 2y agoOoopps... it should read "when H1 is TRUE" not "when H1 is FALSE". Fixing now :) Thanks for spotting this!