Re:Algorithms that select which algorithm should play which ...
by derefr on Wednesday August 10, 2016 @04:55PM
(#12264688)
Why do you think human cognitive processes don't resemble "discrete solvers who's implementations cannot be seamlessly blended"? Humans often get 'caught half-way' between a fight-or-flight response and a calm, analytical solution to a problem, and end up doing nothing. There's no strategy-blending there.
Humans can learn blended strategies by practice, but by doing so they're neuroplastically creating a single new blended-strategy strategy (which can poke at subgraphs of its component strategies' synapses), rather than simultaneously activating two strategies. You can learn to play a game that combines Tetris and a platformer (e.g. Wario's Woods)—and you'll become skilled at this faster if you've played both Tetris and platformers before—but learning this skill won't help you at all in becoming better at multitasking two separate, concurrently-running games of Tetris and a platformer. The synthesized strategy is its own concrete node, not an enhancement of the original nodes to enable efficient blending.
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ETA: I suppose that it might be that "poking at subgraphs" thing that you're getting at, though? When a human learns a game, it gets better at playing all games, because there are individual "subroutines" that can be retried and, if successful, reused to accelerate the learning of other games.
Adding this feature to a GGP model wouldn't require "blending" (i.e. fluent instantaneous synthesis of strategy-components), but it would achieve a similar gain in performance. Programming the GGP algorithm to factor the decision-algorithm implementations it generates into hierarchies of small, modular black-box implementations, rather than large, monolithic black-box implementations, would get you a gradually-building "library of functions" that the GGP algorithm could attempt to "slot in" in new algorithms as it's synthesizing them.
Humans can learn blended strategies by practice, but by doing so they're neuroplastically creating a single new blended-strategy strategy (which can poke at subgraphs of its component strategies' synapses), rather than simultaneously activating two strategies. You can learn to play a game that combines Tetris and a platformer (e.g. Wario's Woods)—and you'll become skilled at this faster if you've played both Tetris and platformers before—but learning this skill won't help you at all in becoming better at multitasking two separate, concurrently-running games of Tetris and a platformer. The synthesized strategy is its own concrete node, not an enhancement of the original nodes to enable efficient blending.
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ETA: I suppose that it might be that "poking at subgraphs" thing that you're getting at, though? When a human learns a game, it gets better at playing all games, because there are individual "subroutines" that can be retried and, if successful, reused to accelerate the learning of other games.
Adding this feature to a GGP model wouldn't require "blending" (i.e. fluent instantaneous synthesis of strategy-components), but it would achieve a similar gain in performance. Programming the GGP algorithm to factor the decision-algorithm implementations it generates into hierarchies of small, modular black-box implementations, rather than large, monolithic black-box implementations, would get you a gradually-building "library of functions" that the GGP algorithm could attempt to "slot in" in new algorithms as it's synthesizing them.