“But the general result is that one can start with an AI with utility/probability estimate pair (u,P) and map it to an AI with pair (u’,P) which behaves similarly to (u,P’)”
Is this at all related to the Loudness metric mentioned in this paper? https://intelligence.org/files/LoudnessPriors.pdf It seems like the two are related… (in terms of probability and utility blending together into a generalized “importance” or “loudness” parameter)
Is this at all related to the Loudness metric mentioned in this paper?
Not really. They’re only connected in that they both involve scaling of utilities (but in one case, scaling of whole utilities, in this case, scaling of portions of the utility).
“But the general result is that one can start with an AI with utility/probability estimate pair (u,P) and map it to an AI with pair (u’,P) which behaves similarly to (u,P’)”
Is this at all related to the Loudness metric mentioned in this paper? https://intelligence.org/files/LoudnessPriors.pdf It seems like the two are related… (in terms of probability and utility blending together into a generalized “importance” or “loudness” parameter)
Not really. They’re only connected in that they both involve scaling of utilities (but in one case, scaling of whole utilities, in this case, scaling of portions of the utility).