| Serious question. Putting aside all the stuff that basically make me feel like you are just calling me a moron and wasting my time: what do you disagree about? You keep treating the things I say like they are irrelevant jargon and not bothering to engage. Can you please try for a moment to explain why you think my argument that error in abstraction is useful is wrong by addressing the actual premises that are within that argument. Literally, address one of these claims: 1. Do you disagree with my claim that the runtime of learning algorithms depends depends on the graph size in both game theory and reinforcement learning problem formulations? 2. Do you disagree with my claim that abstraction reduces the number of states in the graph? 3. Do you disagree with my claim that since abstraction reduces the number of states in the graph the learning algorithms which run against them can complete more quickly because there are less states? 4. Do you disagree with my claim that algorithms which can compute a solution can have a better solution than algorithms which don't compute the solution? 5. Or if you don't disagree, can you admit that we agree on these things? Because when you just act like I'm not making any points it makes me feel like you are trolling me and being a jerk, not actually trying to talk to me. I'm still just as convinced of the truth of the idea that it can be very wise to accept a bad abstraction, one that has error, rather than a perfect abstraction. I can't even fathom how to go about the opposite. How would a child go from knowing nothing to knowing everything perfectly without moving through areas of bad abstraction along the way? Which numbered point do you feel is incoherent? |
- When we tried to solve chess we couldn't, the branching factor was too much.
- Go, it was horrendous there too.
- Poker, terrible there too.
But you want to dismiss me on the basis of jargon right? So here you go. Bellman coined the term curse of dimensionality. Combinatorial explosions happen because of branching factors in game graphs. Computational complexity for algorithms are defined with respect to this graph in both time and space for many learning algorithms. Because the games get so big the curse of dimensionality forces problem relaxation. I used ~words~. I must be an idiot. Feel free to dismiss me, I guess. I heard you heard someone else use words once and they were ~wrong~.
Hey wait a second. You're using words too. Does that mean everything you say is wrong?