The most important feature of AIRIS is its ability to generate predictive models of the environment it is operating in. It then searches those models to determine the best course of action to achieve its goals. In the example below from the normal information complete puzzle environment, you can see how AIRIS plays through each sequence of planned actions in its “mind’s eye” before actually performing the actions in the game. Then as it performs the actions, it follows along with its generated models to make sure that its predictions were accurate.
The importance of this feature is especially evident when AIRIS is operating in an information incomplete environment. In the example below, an untrained AIRIS is put into a puzzle game where it can only see a small portion of the game world at a time. As it explores, you can see how it attempts to generate a predictive model of what unexplored areas may look like. Since these predictions are based on the limited information from areas it has already seen, they are often incorrect. However, at the pause point in the middle of the example you can see that it has learned enough about an area it has explored to be able to start making small plans.
Once it has explored a level, it can then go back and use those memories to make plans to collect the unseen batteries just as easily as if it could see the whole level at once. In the example below, it “remembers” what the level looks like based on its memories from when it explored it. It remembers where the batteries were, and makes its plan to collect them. Then it follows through with its plan while making sure that the level is how it remembered it.
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