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Comment by komaromy | Hacker News Reader

Maybe not the next big advance, but I'll be truly impressed when an AI system with no game-specific prior knowledge is able to complete a game of Pokémon Yellow within a reasonable amount of game time.

It requires:

- Natural language understanding (choosing the right dialog options)

- Interpretation of visual cues (navigating terrain and buildings)

- Hierarchical planning (training certain Pokémon to relatively high levels rather than a scattershot approach)

- Puzzle solving (for gym access)

and quite a bit more.

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Don't be ridiculous. Nobody would waste the effort on Pokemon Yellow.

Everyone knows Pokemon Red is clearly the best Pokemon game of them all.

I would also add to this that once the basic mechanics are learned, it should be much easier for that AI to pick up another similar game (for instance, Pokemon Gold or Silver). Adapting to slightly different but similar environments is a plus.

Technically many of these are not really necessary IMO. The path that is most likely result in a solution to this problem is some scaled up version of Deep Reinforcement Learning (with a novelty reward) without any explicit natural language understanding; though planning will probably be involved in scaling it up and understanding visual cues is already trivial.

I think when thinking about these tasks, the right question to ask is "what's the dumbest way this could be solved?" rather than "what high level knowledge would I use to solve this?", otherwise you will be disappointed when the task is solved but the methods do not look what you would like them to.

You would be surprised how bad actual AI is at games. All kinds of, but especially ones where the goal is far away and you don't readily know you're succeeding. (Definition of a sparse reward game.)

Part of promising system is providing more understanding of output instead of being blind and deaf, but indeed the main decision structure has to handle it at all.

I think the Intrinsic Motivation is pretty important to solve this, and DeepMind has shown it to work well for games, since it makes the reward far less sparse, in a way that is very similar to our own way of playing games - which IMO is somewhat important since we are the intended consumers.

Maybe making generic novelty metrics will turn out to be harder than anything else anyway, but it has some intuitive appeal.

So I guess we're on the same page, we do need to understand the game output, but IMO understanding it well enough to know when things are novel & interesting vs when things are not is probably enough to get quite far.

Depending on the game, it may be done on purpose.

As someone who's made games, released them, and suffered so many complaints about how hard they were, so I'd dumb down the A.I. and still get complaints about how hard it is.

Or another 2 player turn based game that included three different difficulties and had a ton of people telling me easy mode -- which was basically the computer making random moves -- was impossible to beat, and the computer cheated, and the game was stupid, and I should kill myself for making such a stupid game.

A.I. isn't usually super great in games because it doesn't have to be to give a challenge to the vast majority of people.

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