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by sullyj3
3257 days ago
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A search tree in something like chess is quite small, and very discrete. You can enumerate every possible action, and exploring the tree to a useful depth is computationally tractable. By contrast, for an agent operating in a complex environment, like a robot in the real world, even if you somehow came up with a coherent process for listing every possible action the robot could take, you might not even be able to store them all, let alone compute their consequences. Think about the sheer amount of information you'd need to process. Moreover, the real world is (for practical purposes) continuous. The robot would have the option of engaging one of it's motor for one millisecond, or two milliseconds, or three milliseconds, etc. This seems to be tackling the issue of what to do when there are just too many options, and the depth of exploration necessary to make useful predictions is too high, for you to just enumerate everything, heuristically prune, and pick the optimum. |
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Are there not similar techniques to search trees that are used here? Obviously you wouldn't enumerate all options but you'd think you could guess at some practical ones then guess options between the most promising. Either way, it just feels "imagination" is making it sound like an entirely new approach when heuristically pruned search trees could be described in the same way to me.