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  • Hi I'm Tommy Thompson, this is AI and Games and welcome to part 4 of the AI of Total War.

  • In part 3 of this series I explored the campaign AI of one of the most pivotal entries in the

  • franchise: 2013's Total War: Rome II. A game that completely re-built the campaign AI systems

  • to accommodate for an increasingly more complex series of mechanics, resources and consequences.

  • Rome II's adoption of the Monte Carlo Tree Search algorithm is a critical step in bring

  • the campaign AI up to spec for more contemporary entries in the series. But the innovations

  • at campaign level didn't stop there. In this video I'm going to look at how the MCTS systems

  • were improved upon, as well as how the diplomacy systems have been scaled up for the modern

  • era as Rome gave way to 2015's Total War: Attila.

  • Attila is the ninth entry in the Total War franchise and transposes the conflict to the

  • late 4th and early 5th century: a period of history known as the Migration Period. The

  • Roman Empire begins to falter, even fracture; the empire is separated with the latin-speaking

  • Roman Empire to the west and the greek-speaking Byzantine Empire to the east. The Roman Empire

  • is on borrowed time and will only survive another 200 years at best as it is besieged

  • by the vandals, franks, saxons and perhaps most importantly, the huns. Players bring

  • Attila the Huns rise to power as the Hunnic empire forms over what is now eastern europe.

  • Total War: Attila is in many respects a natural successor to Total War: Rome II - with a campaign

  • map of similar size and shape that succeeds the rise of the Roman empire in the previous

  • title, only to see it crack and crumble with a total 0f 56 factions appearing as either

  • ally or foe in the original version of the game.

  • But of course, it's not just the change of setting, the underlying tech continues to

  • change and evolve as well. The introduction of Monte Carlo Tree Search in Rome II, the

  • focus of part 3 of this series, was a massive undertaking. MCTS had not made the transition

  • into AAA gaming at that time and naturally there was still much to learn as to how to

  • utilise the systems full potential. So let's take a look at the gradual changes and improvements

  • made to the algorithm between post-launch updates for Rome II, all the way the launch

  • of Attila.

  • The MCTS was updated to address some notable issues in performance and results. Notably,

  • the algorithm was slow to decide, but was typically making good choices. A big part

  • of this was that state space coverage becomes increasingly difficulty the longer the game

  • progresses: the number of AI factions begins to grow, the number of possible future states

  • grows at an exponential rate. In addition, the underlying pathfinding tools to calculate

  • distances between factions and the like during MCTS playouts was proving very expensive.

  • To resolve this, the algorithm was trimmed to prune the number of strategies it considers.

  • This required additional analysis of a given state using metrics from the developers itself,

  • to determine whether a target was unreachable, a battle was unwinnable, there was a low probability

  • of a strategy succeeding or a given path in the tree was redundant and then removed it

  • from the decision making process. This last part isn't as easy as it might sound, given

  • a lot of strategies are essentially identical: executed in a slightly different order and

  • result in a similar outcome. This required the system to break up searching into sub-phases

  • such that is enforced an arbitrary ordering. This helped identify multiple strategies that

  • essentially the same when ordered a specific way. In addition, this all required aggressive

  • patching of the pathfinding system to pre-cache some calculations and simply reduce the number

  • of calculations taken, only asking if the MCTS decision making process deemed it necessary.

  • This resulted in a system that was more memory efficient and ultimately faster. It shows

  • that the MCTS algorithm, while relatively simple in its design and execution, is also

  • once that requires additional care when tackling large and complex problem spaces. Given it

  • may need some assistance in order to make its decisions more efficiently. Especially

  • when trying to ship it in a AAA product.

  • One area of the franchise I have not yet touched on is the notion of diplomacy: the process

  • by which campaign AI players accept, offer and negotiate trade deals and alliances. Diplomacy

  • is driven by an entire AI system in and of itself. Adding to the steadily growing collection

  • of AI subsystems such threat analysis, pathfinding and siege battles, which I will cover in part

  • 5. Diplomacy is primarily interested in answering three key questions for the campaign AI on

  • a given turn:

  • It

  • needs to ensure the deal is valuable to both parties. This is both valid for an incoming

  • or outgoing deal, given it wants the other faction to accept its proposal, but conversly

  • it may need to haggle an incoming proposal to its satisfaction.

  • The Diplomacy system is driven by the same information and logic as the player, it can't

  • take any shortcuts, even when two AI factions are dealing with one another. They still go

  • through the same process as a player would either with an AI or another player. This

  • is a highly data-driven process, with each faction having specific and unique configurations

  • of values that drive diplomacy in an effort to ensure they all behave slightly differently.

  • This is pretty important in deriving their personality throughout a given campaign, which

  • I'll come back to discuss a little bit later.

  • In order for the AI to pull it off, it transforms these three key questions into diplomacy sub-systems

  • (or sub-sub-systems I guess). Deal evaluation, generation and negotiation. Each of these

  • systems utilises four key metrics that are generated by the system given the incoming

  • information it has received:

  • - The economic value: Is this deal going to actually benefit me monetarily?

  • - The stance value: Do I even like these guys who are negotiating with me?

  • - The strategic value: Will I gain a powerful ally from this deal? This is important for

  • war and peace declarations, given the current threat as perceived on the campaign map (done

  • so using influence mapping) can help determine whether this will make life easier, or more

  • difficult. - And the diplomatic value: What will other

  • factions think of me if I sign into this treaty?

  • If diplomatic value is negative, it means that everyone else *really* won't like you

  • signing this deal. In addition, things like stance and strategic value are influenced

  • by a balance factor that is hand-tweaked by designers. The balance factor is incorporated

  • into the difficulty and in-game progression, such that when an AI is losing badly, it'll

  • be more likely to accept offers that might not even by that useful, even from people

  • they don't really like. Given it may help ensure their survival.

  • For deal evaluation, all of these values are then pumped into a weight summation function

  • to give what is called the Deal Value: if the deal is scored as higher than 0, it means

  • that it is worth the AI player accepting that deal.

  • Meanwhile in deal generation, it needs to make some smart decisions on what to offer.

  • The list of all possible diplomatic actions is took long to consider, as such the system

  • will use a pre-filtered list based on the current strategic situation, factoring strategic

  • and economic values pertinent to the current state of the game. It then prioritises each

  • deal by evaluating it, with the actual evaluation shifting throughout gameplay, given that a

  • deal may have more or less value at different times during a given game.

  • Lastly, the system negotiates deals by starting with a collection of generated deals that

  • it evaluated as useful. It will then begin to offer these to other players, but is mindful

  • of previous offers it has made with a given faction. If it receives an offer that the

  • deal evaluation scoring disliked, then it will make a counter offer with a given probability

  • or simply reject it. The AI players are made to weight a certain number of turns before

  • attempting diplomacy with a given faction again, so as to avoid spamming you with new

  • offers every 2 minutes. The actual discussion between faction leaders that takes place during

  • negotiation is powered by a separate system that ensures that the dialogue fits the style

  • of the people of a given faction.

  • Speaking of AI factions, by the time all of the DLC was released for Attila, there was

  • over 80 factions in the game. So how do you make all of this diplomacy not feel stilted

  • or samey with each and every faction you deal with? As mentioned before, the diplomacy system

  • is data-driven, so each faction is fed data that influences how it behaves in areas such

  • as budgeting, diplomacy evaluations and negotiations and technologies. These components craft what

  • Creative Assembly consider to be AI personalities. Each of these components are often quite extreme

  • and binary, with budgeting making the AI range from Scrooge McDuck to Kanye West. Each of

  • these personality traits are effectively communicated via the user interface, such that players

  • can establish just how to deal with a given faction.

  • But it's not enough to have just a static personality throughout a given campaign, it

  • needs to change and evolve over time. As such, each faction has a personality group with

  • multiple personalities it can choose from. The choice of selection which personality

  • it wants to use is driven by a number of factors, such as the current difficulty of the game

  • and the stage of the campaign itself. As such, ultra-aggressive BURN IT ALL DOWN style personalities

  • don't appear as frequently at the start of the campaign, but are more likely to appear

  • towards end-game. In addition, many of the personality swaps are tied into the progression

  • of faction leadership: with new personalities adopted as a faction leader dies and their

  • heir comes to power.

  • While deciding how to conduct war can be challenging, so to can be forging alliances and making

  • peace with others. The scope and complexity of Total War continues to be vast in scale,

  • to a point that the number of minor AI systems continues grow in order to support the unit,

  • battle and campaign AI. But diplomacy isn't enough in times of conflict, sometimes we

  • just need to force our point of view on our enemies. To make them bow to our will. In

  • part 5 of my exploration of the AI of Total War, I'm going to look at another of the AI

  • subsystems in play: one that is responsible for laying siege to an enemy fortification.

  • While these have been critical to the series for many iterations, I'm going to take a look

  • at the more recent innovations brought to this system alongside some fresh perspective

  • for the franchise: as history is cast aside for fantasy and we enter the world of Warhammer.

Hi I'm Tommy Thompson, this is AI and Games and welcome to part 4 of the AI of Total War.

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全面戰爭:阿提拉》中的外交人工智能(4/5)|人工智能與遊戲 (The Diplomacy AI in Total War: Attila (Part 4 of 5) | AI and Games)

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    wei 發佈於 2021 年 01 月 14 日
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