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Hi I'm Tommy Thompson, this is AI and Games
and welcome to part 3 of the AI of Total War.

As the core systems of Total War have been
established and redefined in the franchise

- a point I have discussed in the first two
parts of this series - there is always a need

to strive for better. RTS games continue to
be one of the most demanding domains for AI

to operate within and as such we seek new
inspiration from outside of game AI practices.

With this in mind, I will be taking a look
at 2013's Total War: Rome II - one of the

most important games in the franchise when
it comes to the design and development of

AI practices. So let's take a look at what
happened behind the scenes and what makes

Rome II such a critical and vital step in
Total Wars future progression.

In part 2 of this series we concluded with
an overview of the dramatic changes to the

underlying AI systems in Total War with the
release of Empire, followed by Napoleon in

2009 and 2010 respectively. What was once
a more simple and more manageable state-driven

and reactive AI system had made way for an
adoption of the Goal Oriented Action Planning

system. A technique popularised by First Encounter
Assault Recon. The GOAP implementation within

Total War was ambitious but struggled on launch
with Empire, requiring patching and updating

both post-launch as well as in the following
year's Napoleon. The same AI tech was adopted

in 2011's Total War: Shogun 2, with it proving
to be a less challenging experience for the

systems involved. Shogun 2 returned to Japan,
which provided a much more balanced mix of

ranged combat and melee, with less emphasis
on gun-driven combat. Even the campaign AI

didn't struggle with the same problems as
Empire and Napoleon, with a smaller and less

chaotic structure. But while it seems Creative
Assembly was becoming content with the combat

systems, the campaign AI still needed more
work. This resulted in some significant changes

under the hood during the Fall of the Samurai
DLC for Shogun 2, which among other things

includes the naval warfare of Empire and Napoleon.
One of the new problems this creates for players

is that the army and naval logic were until
that point separate, meaning the AI needed

to be rewritten to consider how naval strategy
could influence ground troops, such as being

bombarded on the coast line. At that point,
the campaign AI's planning approach couldn't

foresee these issues well enough and was often
stuck being reactive in its planning process

rather than deliberative and forging ahead
on its own ambitions.

To resolve this, a new campaign AI system
was prototyped in Shogun 2, which was later

expanded to create some rather seismic changes
in Total War: Rome II.

2013's Total War: Rome II was a return to
one of the most well-known entries in the

franchise, but with it came a rather seismic
change for the campaign AI under the hood.

The drive for a more deliberative system that
could consider the overlap between mechanics

resulted in a growing number of sub-systems
responsible for individually managing the

budgeting of money, conducting diplomacy,
selecting tasks for attacking and defending

- be they attacking enemy forces or laying
siege to settlement - deciding what issues

take high priority, figuring out how to navigate
an army safely across the map, not to mention

managing construction and taxes.
All of these require the AI to consider the
overall suite of resources it has at its disposal

and how best to utilise them. The system is
still reliant on the belief, desire and intention

system mentioned in part 2, but now the sheer
number of combinations here are staggering.

Even if the system has decided on a smaller
subset of tasks it wants to complete in a

given turn, there are still tens of thousands
of different possible outcomes for that one

turn. The map for military deployment is quoted
to have around 800,000 individual hex points

alone. How can the system hope to approach
this sort of task at this scale?

The answer comes in the form of Monte Carlo
Tree Search: an AI algorithm that had recently

taken academic research by storm and is making
big waves in general intelligence AI research.

MCTS allows for the system to consider all
of the different possiblities, explore the

ones that seem the most fruitful but also
continue to consider alternatives. In time,

those alternatives might yield some strong
outcomes, so this system is able to keep doing

things it knows are good for it, but also
consider other opportunities along the way.

Now before we get into the meat of how the
campaign AI in Rome II is managed through

MCTS, I need to take a moment to talk about
how the algorithm works.

Monte Carlo Tree Search is a type of reinforcement
learning algorithm: a branch of machine learning

algorithms that look at a problem and find
good decisions by considering all possibilities,

while largely focussing on the ones it finds
to be most useful. This is really useful when

you have a problem that is incredibly large
and has a large number of possibilities, given

we might find a good decision to make, but
we can't say with any certainty it's the best

decision. In order to have a better understanding
of whether there are better options to take,

we need to consider alternatives periodically
and see if they would be more useful. This

is known in reinforcement learning as the
exploration/exploitation trade off. We want

to exploit the actions and strategies we have
found to be the best, but must also continue

to explore the local space of alternative
decisions and see whether they could replace

the current best. This is a difficult process
to resolve, given that sometimes we need to

really explore a series of decisions to discover
that an action that might look bad now, might

actually prove to be a really good idea somewhere
down the line.

This is what MCTS does best: it explores all
potential options for a given decision point,

isolates the best ones and then dictates which
one is the best, both considering it's short

and long-term ramifications.
The key component of MCTS the ability to run

a playout: where the AI effectively plays
the game from a given starting point, all

the way to the end by making random decisions.
Now it can't actually play the game to the

end, so MCTS uses what's called a forward-model:
an abstract approximation of the game logic

that allows it consider the outcome of playing
action X in state Y, resulting in outcome

Z. The algorithm gathers up all the decisions
it can make in a given state of the game,

then runs thousands of random playouts across
them in a structured and intelligent fashion.

It gathers data from each of these rollouts
and concludes the process by selecting the

action that had the best rollout score. It's
both incredibly powerful and strangely stupid

in its execution.
The smart part comes in how each rollout is
decided upon and executed, to do this it relies

on four key steps: selection, expansion, simulation
and backpropagation.

Selection takes the current state of the game
and selects decisions down the tree to a future

state a fixed depth down the tree.
Next up comes expansion: provided the state

we reached didn't end the game (either as
a win or a loss), we expand it one step down

to and simulate the outcome.
Simulation is the random playout phase: it

plays a game of completely random decisions
from this point until it reaches either a

terminal state (where it wins or loses) or
a simulation cap is reached. It then gives

back a result of how well it performed as
a score. This is passed to the backpropagation

In backpropagation: we update the perceived

value of a given state, not just to the state
we ran the rollout, but every state that led

to it. So any score - be it positive or negative
- works its way back up the tree to the starting

Through those four phases, we can take decisions
to a fixed point in the tree, simulate their

outcome and then propagate back the perceived
value of it. Now doing this once isn't enough,

you have to do it thousands of times and balance
which playouts to make. Different MCTS algorithms

balance it out so they shift focus to different
parts of the tree periodically to ensure there

are no better solutions to be found it didn't
otherwise spot. But once the playout limit

is reached, it's done and takes the action
leading to the best scoring state.

What makes this system even more powerful,
is that it's what we call an anytime algorithm:

meaning that it will always give an answer
regardless of how many playouts we let it

take. So in a context like a game, where CPU
and memory resources are pretty tight, if

it needs to stop evaluating the game at a
moments notice, it will still give the best

answer it could within that time. Despite
this, giving it a massive amount of CPU resource

won't result in godlike AI, given the knowledge
accrued from repeatedly running playouts eventually

levels out.
Alright, with all the science out of the way,
how does this all work in Rome II?

First I need to explain how the Rome II campaign
AI manages itself. It's broken down into three

chunks: pre-movement, task allocation and

- Pre-movement identifies threats and areas
of opportunity for the player. It also budgets

resources, conducts diplomacy and selects
skills for armies.

- Task allocation is conducted by a highly
complex Task Management System - which is

the focus of the MCTS. The task system handles
armies, navies, agents and actions related

to diplomacy.
- Lastly there is post-movement: once all

units and such are moved and decisions made,
the AI will then focus on construction of

buildings, setting taxes and technology research.
MCTS is responsible for managing two critical
components of the task allocation systems:

the distribution of resources such that the
AI can approach different tasks it wants to

complete and the execution of specific tasks.
The tasks themselves are driven by a variety

of different task generation systems with
their own focus or perspective. So while there

is a task generator for armies, there is also
once for navies, diplomacy actions and much

more. The thing is that there are often way
more valid tasks to execute than there are

available resources: the actual units on the
map and money to spend. As such, the system

then prioritises which tasks it would complete
by selecting the most viable and then allocating

resources to them.
In addition, task viability also carries some
filtering to stop it trying to do anything

too stupid, such as removing actions that
could cause diplomatic tensions, filtering

actions that could impact long-term strategies
and also factoring what it had done recently

so it avoids contradicting itself. Once filtered,
the tasks are then assessed using the MCTS

algorithm to grade their effectiveness and
priority. With the best and more desirable

looking opportunities graded a higher priority.
After this, the MCTS is called on again in
order to run resource coordination: or rather

now that it knows what it wants to do, it
still needs to figure out how exactly to do

it. As such, once the system has made some
approximations of appropriate targets and

their locations on the map, it will run more
MCTS approaches on army movement and army

recruitment. Factoring the makeup of its own
forces as well as the opponents in order to

determine where best to move current forces,
as well as what types to recruit for future

In each case, the MCTS is limited such that
it doesn't search all the way to the goal,

given that Total War as a game is so large
that it would take too long for it to simulate

completing the game. In addition, the game
is complex that simulating that far out won't

yield any useful outcome. In fact, it was
quoted that the system is only capable of

looking one turn ahead before starting random
playouts due to the complexity of the game.

Given the nature of Total War, the MCTS can
only exhaustive search the entire state space

for the best action during the opening turns
of the game. Over time the number of possible

states grows exponentially, to a point that
it is simply beyond the algorithms reach.

Despite this, the anytime property of the
algorithm ensures we will still get a useful

and intelligent decision from the system.
Rome II launched in September of 2013 to a
largely positive response, but with a few

problems. Most notably, the campaign AI took
quite a long time to make its decisions in

the launch build: taking several minutes to
conduct campaign movements that most players

conduct in a minute or two, resulting in aggressive
patching of the game for several weeks after

launch. In time this led to a noted improvement
in campaign decision making that was received

favourably (though not univerally) among fans
and critics.

Revolutions aren't easy, nor are they clean
and the legacy of Total War: Rome II is no

exception. But it is nonetheless a major milestone
for the development of AI systems and practices

in the commercial video games and has led
the way for many a successor that is seeking

to adopt MCTS as part of its own AI toolchain.
MCTS is a hot topic in contemporary AI research

and has shown many useful applications in
fields of expert play and general intelligence.

To learn more about how it all works, be sure
to check out the AI 101 on MCTS here on AI

and Games.
Thanks for watching this third entry in the
AI of Total War. In part four, I'll be looking

at how the MCTS implementation was improved
in Total War: Attila, combined with a deep

dive into just how exactly does the diplomacy
AI work in more recent iterations of the game.

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Behind the Campaign AI of Total War: Rome II (Part 3 of 5) | AI and Games

91 Folder Collection
wei published on December 16, 2018
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