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ErUs

learning how to play a game.

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ErUs    136
are there any example of ai that learns to play a game? for example ai that is aware of some inputs and outputs but has no hard-coded knowledge of what is required to win a game. It would then try to work out why he was failing to reach an objective. sort of thing.

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databandit    124
I think you could make it similar to pathfinding. You could give the system a goal and a start but it has to find his way through the obstacles (in this case, mistakes)

-Zubair-

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deftware    1778
you need to at least be able to tell your system something akin to pleasure and pain, right and wrong, yes and no.

then devise a system that will learn through trial and error how to react to different situations in terms of your pain/pleasure paradigm system.

in the case of pac-man i would hardcode my system to sense pleasure in eating the little yellow balls, whereas dying would cause pain.

pleasure merely re-inforces the last act that took place before the pleasure occured.

pain jumbles perception/thought giving a sense of void and anti-reinforcement in the last action.

it could end up being a huge project, but it would be worth it if you made it play well enough to play multiplayer, hehe.

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birdtracker    136
The biggest issue that see is when the actions that lead to a pleasure inducing or pain inducing state are complex and/or time delayed, it is difficult to decide what to reinforce.

In a situation where a single action produces a reward, such as "move towards food" = good, the implementation is simple. If it is a situation where you have multiple inputs and multiple rewards, such as "move left wheel left, right wheel right, apply gas, remove brake" => reduces distance to nearest food but also reduces distance to nearest enemy = bad if enemy is close, it gets more difficult to determine what to reward.

This is where it is nice to have genetic algorithms to do the fitness testing for you, but that doesn't solve the problem of teaching the current agent what to do.

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AllTheLuck    126
I don't mean to sound lame, but you should really check out some of the books on the subject of AI. There has been a ton of study in the subject of training an machine learning. In particular neural networks are very good at pattern recognition.

The concept of breaking the problem up into a state space is a good and simple abstraction of the problem, but I think that you will find that representing learning in a dynamic way is a very complex task. I do not know very much about it, but I hope that you are successful.

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