I’m already aware of that paper, but it seems to me that MC-AIXI is more similar to MC tree search than to SI. I’m quite impressed with the effectiveness of MC tree search for Go.
it seems to me that MC-AIXI is more similar to MC tree search than to SI.
I’m not sure it’s entirely meaningful to say they are very different. A tree of quasi-programs with weightings over them and a random playout for the point at which you have to stop looking deeper. A computable approximation has to be computable, after all, so it’s not too much of a surprise if it reuses computable techniques that have been found to be effective in specific domains.
Ok, they both tree search over a space, whether it is the space of strategies or the space of programs. That does make sense.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy. By comparison, I could imagine some kind of tree search, possibly weighted by heuristics, to be efficient.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy.
It’s crazy, but some universes are crazy. Pity the poor AI who wakes up inside a simulation where the programmer is in fact testing it on every program of length <n!
I’m already aware of that paper, but it seems to me that MC-AIXI is more similar to MC tree search than to SI. I’m quite impressed with the effectiveness of MC tree search for Go.
I’m not sure it’s entirely meaningful to say they are very different. A tree of quasi-programs with weightings over them and a random playout for the point at which you have to stop looking deeper. A computable approximation has to be computable, after all, so it’s not too much of a surprise if it reuses computable techniques that have been found to be effective in specific domains.
Ok, they both tree search over a space, whether it is the space of strategies or the space of programs. That does make sense.
I think my initial reaction to SI was very negative—even without the halting problem, simply testing every program of length<n is crazy. By comparison, I could imagine some kind of tree search, possibly weighted by heuristics, to be efficient.
It’s crazy, but some universes are crazy. Pity the poor AI who wakes up inside a simulation where the programmer is in fact testing it on every program of length <n!
We could be in a simulation where the programmer is in fact testing it on every program of length <n! Doesn’t seem so bad.