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In this example, I'm going to
use the same environment to show

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how we can avoid the slow model
even more using multiple queues.

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So the main difference is that
there is an anchor search, which

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does the same procedure as the
previous planner did of picking

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the fastest model and the
precondition. And this is used

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for completeness. And then we
also have additional searches

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for each of the faster models.
So in this example, we only have

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two models. So there is an
additional search that only

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compute successors using M1

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We start by adding s0 to all of
the open queues.

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And then we expand s0, this
tiebreaker expands from the

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faster, faster queue, and then
we add s0 to all of the

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graphs, then we compute the
successors. But when we're when

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we're expanding in search one,
we only compute successors using

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M1. So the action needs to be
in the precondition. Luckily,

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the successors of s0 SA and
SB can be computed with M1.

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And then we close S0, then we
expand SA, based on that tiebreaker

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there's only one action

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which is a0, and that has to be com
uted using M0 because SA an

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(sA, a0) are not in the preco
ndition of M1 So what we do

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here is we add it to this a inc
omplete which you can see unde

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the cost in for sA in open0
and an open1 So that

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way, it can be evaluated late
by other searches.

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And then we close sa but we only
do that in search one, so it

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can still be expanded in search
zero.

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Next, we expand SB. SB has two
successors, it has

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a1 or a2. However,
SB a2 is not in the

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precondition of M1. We do find

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find a path to SG, by executing
a1 from SB

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and now we add SG to all of the
open queues. And finally we expand

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the goal state and we are able
to and when we expand the goal

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state we're able to find a path
from start to goal that was the

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same cost as what we found
before but without having to use

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the slower model.

