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  • Hi I'm Tommy Thompson, this is AI and Games and this is part 2 of the AI of Total War.

  • In part 1 of this series I looked at Creative Assembly's 2000 release Shogun: Total War

  • - a game that redefined real time strategy games. Shogun defines three specific layers

  • of AI systems: the unit AI that controls individual troops and keeps them in formation and on

  • point, the combat AI that groups and sets formations to units and the diplomacy AI that

  • conducts the turn-based strategy seeking to take control of feudal Japan. I concluded

  • part 1 with 2002's Medieval: Total War: a game that refined and improved the core systems

  • Shogun established. But this is just the beginning of a long journey improving and rebuilding

  • the AI systems behind the franchise, both from within The Creative Assembly itself,

  • but also from the Total War fanbase. So let's look at the subsequent releases in the franchise

  • and watch the evolution of war take place.

  • 2004's Rome: Total War brought both gameplay changes as well as a significant graphical

  • overhaul. This third entry in the series brought a 3D diplomacy map as well as fully rendered

  • 3D units to the franchise as it moved to 270BC Italy: the birth of the Roman empire. It's

  • the entry that propelled the series from niche strategy sim to a full-blown blockbuster IP.

  • However, it's also where some of the cracks were starting to show in the gameplay. This

  • is most notable in the campaign segments, where the AI struggles to be as effective

  • in this richer and more interesting set of diplomacy mechanics. In part this is can be

  • attributed to the longer and more thoughtful progression of actions that are needed in

  • order to build and scale your own corner of the Roman empire. As mentioned in part 1:

  • the campaign AI is state-based with use of genetic algorithms often to provide some variety

  • in respective AI players and their decision making. Neither of these two systems are ideal

  • when you need to consider long-term decision making, given these approaches seldom consider

  • the history of actions previously taken and the long-term ramifications.

  • Despite these issues, it was still a valuable and fun entry in the series and one that has

  • developed a very strong fan following. Rome is also the beginning of a long and storied

  • history of mods being built for Total War. This was realitvely easy for a modder to accomplish

  • given that the earlier entries in the series left much of the data that drove gameplay

  • exposed in the installation. As such modders could then build tools that manipulated that

  • data for their own purposes. Many of these mods aim to improve graphics and controls,

  • expand the narrative and create a richer world within which to play, such as Roma Surrectum

  • and Rome: Total Realism. Some even take the core mechanics and AI and transpose it to

  • other worlds, such as The Fourth Age - a Tolkien mod - and ironically, the Warhammer: Total

  • War mod. Arguably the most impressive of these is DarthMod: the first in a series of mods

  • by Nick Thomadis running from Rome: Total War all the way to Total War: Shogun 2 in

  • 2011. DarthMod not only tweaks the presentation and gameplay, but often makes significant

  • changes to the parameters that influence the underlying combat and campaign AI behaviours

  • and gives more experienced players a run for their money: resulting in new combat formations,

  • tweaked performance stats and more resilient and aggressive AI behaviour.

  • The underlying issues that arose courtesy of the revamp in Rome reached their peak with

  • the release of Medieval II: Total War in 2006. Medieval II is in essence a revamp of Medieval

  • Total War in the Rome engine, but many of the underlying structural issues with the

  • AI still held ground - but the thriving modding community largely made up for it. As such,

  • it was time for a change, the AI systems needed to be rebuilt from the ground up, which is

  • what happened with the release of Empire: Total War.

  • While Rome was the revamp aimed at refreshing the core systems and gameplay, 2009's Empire:

  • Total War sought for loftier goals. A game aimed not only innovating on the core formula

  • of Total War, but also to make it more accessible to a larger audience. This led to a refresh

  • of the the UI systems, hints and tutorials as well as core components of gameplay. Battle

  • introduced a large variation of units that were reliant on gunpowder weaponry, such as

  • cavalry, musketeers, rifleman and heavy artillery and in conjunction with this, a loose cover-based

  • and navigation system was introduced for troops, allowing them to quickly scale small pieces

  • of terrain and take cover during heavy fire. The largest addition to Empire is the transition

  • to real-time naval combat, where players take command of a fleet of ships and attack opposing

  • forces. In addition, the core campaign takes place across a much larger domain: with the

  • American continents, Europe and India all prominent locations, not to mention the sea

  • lanes that connect them. To make things even more complicated, by this point the campaign

  • AI now needs to consider army and naval resource management, spatial analysis of the game map,

  • recognise enemy threats on different terrains and configurations, conduct diplomacy, manage

  • and allocate its resources as well as work on construction, taxes and more.

  • Undoubtedly, this has led to a huge rise in the scope of the franchise and with it came

  • aspirations for improvement of the underlying tech. Empire completely rewrote the Rome:

  • Total War engine from the ground-up, resulting not only in a new suite of AI implementations,

  • but also some changes to how the game is delivered to users, which had a huge influence on the

  • subsequent modding tools.

  • Empire Total War rebuilt both the campaign and combat AI to migrate away from the purely

  • reactive and state-driven behaviour: whereby the systems would largely respond in kind

  • to decisions made by opposing players, but with their own flavour driven by specific

  • configurations and parameters. For Empire the focus was on bringing the AI to a point

  • it could consider more long-term ramifications as well as balance multiple objectives at

  • once. This led to the adoption of the Goal Oriented Action Planning method: a technique

  • popularised by First Encounter Assault Recon and used in titles such as Fallout 3, S.T.A.L.K.E.R:

  • Shadow of Chernobyl and Deus Ex: Human Revolution. GOAP is a method of classical planning: whereby

  • agents use an abstract model of the world in order to make a series of decisions that

  • will transform the world to a desired outcome. It's ideal for situations where we have a

  • number of individual actions in a sequence that we wish to complete and can reason amount

  • multiple objectives at the same time and execute actions accordingly to address them, provided

  • they do not conflict with one another. Many of the original tactics from Shogun could

  • transfer here quite easily, given that the Art of War logic could easily be encoded into

  • a planning-style language for search and execution. If you want to know more about AI planning

  • and how it works, be sure to check out my case study on F.E.A.R. and Goal Oriented Action

  • Planning, as well as the use of both GOAP and Hierarchical Task Network planning in

  • High Moon Studio's Transformers games.

  • One of the biggest aspects of this release came from how the AI is modelled within the

  • game itself. In Rome: Total War - the game AI was mashed within the logic of the game

  • itself. Meaning it was part of the game and could potentially be capable of doing things

  • that players could not or have access to information it shouldn't. In this and future instalments,

  • the campaign AI is separated such that it now actively plays the game like a human does,

  • with interface hooks in the code base that allow for it to talk to the game and vice

  • versa.

  • The campaign AI is driven by considering three key questions:

  • - How well am I doing right now? - What can I do next?

  • - What resources can I allocate to that?

  • These three questions allow for the use of a Belief Desire Intention or BDI system. Meaning

  • that the campaign AI models a set of beliefs, desires and intentions that drive its decision

  • making processes. Beliefs give an understanding of the world to the AI player, but with the

  • proviso that they might not actually be true, such as where enemy resources are or their

  • relation with a another faction. The desires represent the motivational drive of the player

  • and will set the goals of what the system wants to achieve, both immediate and long-term.

  • This can include things such as capturing or defending territory, stopping enemy agents,

  • recruiting armies, prioritising construction or conducting diplomacy with neighbouring

  • factions. This last one is super complicated, I'm going to come back and talk about it in

  • more detail in a future video in this series. Lastly, the intentions represent the deliberative

  • state of the agent: meaning once it has chosen to do something, these intentions are set

  • for it to continue to achieve, even if it gets sidetracked prior to completing them.

  • This works within a planning-based system given it can continue to monitor unresolved

  • goals and try and work towards them while planning to resolve immediate concerns. However,

  • as will be discussed in part 3 of this series, the system was incapable of considering many

  • of the overlaps that occur with some of these actions and the conflicts they create in the

  • systems own internal decision making, lead ing to another campaign AI upgrade a few years

  • later.

  • In combat the AI systems carry a number of goals that allow them to be more effective

  • in not only completing their mission, but looking after their own resources. So while

  • an active combat goal may be to attack a given unit up ahead but it may also have a goal

  • to ensure its right flanks is secured given the position of the opposing force. These

  • two goals would be re-balanced and prioritised depending on what was happening in the game

  • at that time: with the current size and positioning of its own army, the current actions its executing,

  • the enemy terrain and what an assessment of the opposing enemy being taken into consideration.

  • While goals looking towards attacking the enemy may change or be removed once completed,

  • the defensive goals would continually shift based on the current situation and all of

  • these goals are prioritised by the current state of the battlefield. This results in

  • a system that even when a current plan of execution is interrupted due to shifting priorities

  • (perhaps in a brief effort to defend itself by manoeuvring, or it may need to save its

  • general from a flanking attack), it will ultimately be able to go back to the original goal and

  • resume from where it was before provided it can resolve them. Conversely, if the AI is

  • attacking the player, it won't make reckless decisions - such as breaking its artillery

  • off from the wide flanks in order to attack a different target than the main army - if

  • it's going to pose a significant threat to the survival of the rest of its forces.

  • Empire: Total War was released in February in 2009 but sadly to mixed results. At launch

  • both the campaign and battle AI struggled under the weight of the problems it faced.

  • This isn't terribly surprising: planning for large-scale problems of this nature is incredibly

  • taxing and there is only so many possible outcomes or situations that developers can

  • anticipate during the testing. As Creative Assembly creative director Mike Simpson stated:

  • This AI is not like any other we have written... [It's] by far the most complex code edifice

  • I've ever seen in a game. I wrote much of the campaign AI for Shogun and Medieval I

  • (Ahthose were the days…) and I know that even quite simplestaticevaluate-act

  • AI's with no plans or memory can be complex enough to exhibit chaotic behaviour (we're

  • talking about mathematicalbutterfly effectstyle chaos here). It does what it does, and

  • it's not quite what you intended. This can be a good thingyou cull out the bad behaviours

  • and are left with just what is good, and with a simple system that's not too predictable.

  • The Empire AI is way more complicated than any of our previous product... The net result

  • is an AI that plans furiously and brilliantly and long term, but disagrees with itself chronically

  • and often ends up paralysed by indecision."

  • Creative Assembly attacks this problem consistently for six months after launch, patching the

  • AI up until version 1.5 of the game. These improvements were transferred across to 2010's

  • Napoleon: Total War, which was in many respects a functionally identical game taking place

  • in a different combat theatre. This was in part due to the fact that the campaign and

  • battle AI teams were constantly going back to fix problems in Empire while Napoleon was

  • in development. Hence there are only small changes and improvements in the next release.

  • Meanwhile the modding for Empire hit something of a stumbling block given the change of engine.

  • In previous entries, Total War shipped with many of the underlying variables and performance

  • settings as external data and was loaded into the game on starting up, making it relatively

  • easy for mod tools to be built that could expose and customise it. While ideal perhaps

  • in the days of Shogun, this was simply unsustainable as the size and scope of each game increased:

  • with increased campaign maps as well as more and more tactical combat affordances. It's

  • been quoted that the increased amount of data, combined with expected memory limits of players

  • computers, resulted in significant performance bottlenecks prior to