I wouldn’t count gravity (or the pinball machine) as an optimization process, and I think Elizier would say the same, only better. Once you start saying that gravity, gas expansion etc. are optimization process, it kinda sounds like everything is an optimization process, which isn’t very useful.
The pinball machine question is still usefuln because it helps refine the concept. I’d say the difference between the pinball machine and Kasparov is that once you know the probability distributions for every individual “choice” of the machine (whether a ball will bounce left or right, etc.), you know all about the machine and can use those distributions to prove the ball will reach the bottom. (If it’s a bit hard to imagine for a pinball machine, so imagine a simplified model with a bunch of tubes going around, and some “random” nodes where the ball may fall left or right—like a pinball or patchinko machine, but with a countable number of paths)
Unlike the pinball machine, Having a probability distribution for each of Kasparov’s choices isn’t enough to predict the result of the Kasparov-Elizier game.
You need to know that Kasparov is trying to win to predict the result.
There’s no equivalent knowledge for the Pinball machine—having a model of each of it’s possible “choices” is enough.
I wouldn’t count gravity (or the pinball machine) as an optimization process, and I think Elizier would say the same, only better. Once you start saying that gravity, gas expansion etc. are optimization process, it kinda sounds like everything is an optimization process, which isn’t very useful.
The pinball machine question is still usefuln because it helps refine the concept. I’d say the difference between the pinball machine and Kasparov is that once you know the probability distributions for every individual “choice” of the machine (whether a ball will bounce left or right, etc.), you know all about the machine and can use those distributions to prove the ball will reach the bottom. (If it’s a bit hard to imagine for a pinball machine, so imagine a simplified model with a bunch of tubes going around, and some “random” nodes where the ball may fall left or right—like a pinball or patchinko machine, but with a countable number of paths)
Unlike the pinball machine, Having a probability distribution for each of Kasparov’s choices isn’t enough to predict the result of the Kasparov-Elizier game.
You need to know that Kasparov is trying to win to predict the result.
There’s no equivalent knowledge for the Pinball machine—having a model of each of it’s possible “choices” is enough.