
First draft of the new Artificial Intelligence and Games textbook available now - togelius
http://gameaibook.org/
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jonbaer
I would like to find books which pertain to just this problem alone (build
order planning) ...

"Another sub-problem of the wider StarCraft (Blizzard Entertainment, 1998)
playing problem is build order planning. The problem here is in which order to
build certain improvements to the player’s base and in which order to research
certain technology, a complex planning problem at a considerably higher level
of abstraction than micro-battles. Here, Weber et al have data-mined logs of
existing StarCraft (Blizzard Entertainment, 1998) matches to find successful
build orders that can be applied in games played by agents."

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yorwba
I think the literature on instruction scheduling might also be relevant for
build order planning. You have a set of things to build (= instructions to
execute) in the shortest amount of time, using the available production
facilities (= execution units). Except in most games you can build additional
factories, which doesn't really have an equivalent in CPUs.

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posterboy
> in most games you can build additional factories

You can't just buy addition execution units? Sure there are limiting factors -
in games as well.

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yorwba
Which scheduler automatically inserts instructions to add more ALUs to its
CPU? ;)

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posterboy
Well, I did take a step back from the instruction pipeline analogy, but it's
still covered by operations research and mathematical optimization.

The cost of waking a sleeping CPU core is perhaps comparable to the cost of
building a factory in-game. Whereas, building a ton of factories that can't be
saturated by the incoming cash money is comparable to building a superscalar
processor architecture that can't be saturated by the cache memory.

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CameronBanga
I know it's a draft, but it's kinda annoying to have a big watermark of DRAFT
over every page.

I don't know if a better way to do it, but really makes it tough to jump in
and commit to giving a read through.

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deepnet
Togelius is pure mental (in a good OOTB way) his evo-devo HyperNEAT approach
to problems intractable to random-walk SGD learning learning leaps over snares
and pitfalls, bypasses cul-de-sacs and avoids oubliettes -traversing the whole
search space by a sort of teleportation - a fast global search.

Backprop can then refine the most efficient net architectures. Curiously some
evolve structures akin to LSTM units.

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nishnik
From one glance I see that it doesn't covers neural network architecture or
it's not taking about RL agents. What this book is about?

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otoburb
You may have been looking at an earlier (private?) draft revision of the
guidebook that had those sections omitted.

The draft book that is linked shows RL discussed in Sections 2.6 (pp. 75-79)
and 3.3.2 (pp. 122-125), while Neural Networks are briefly covered in Section
2.5.1 (pp. 62-74) with a note about DeepMind's DQN RL agent on p. 91.

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nishnik
Thank you

