Volume #50 Ready or Not Development Update?

第五十期 嚴(yán)陣以待開發(fā)進(jìn)度更新

Attention Officers,

各位警官們，請(qǐng)注意：

Welcome to the 50th edition of our biweekly newsletter! To mark this milestone we will be having an extra-long newsletter, we’ll be featuring a meaty write-up of the AI system that Ready or Not uses as well as a good public look at the other half of the Station Rework

歡迎來到我們第五十期的雙周簡報(bào)！為了紀(jì)念這個(gè)里程碑的時(shí)刻，我們這次準(zhǔn)備了一篇超長的內(nèi)容給到大家，分別是嚴(yán)陣以待所用AI系統(tǒng)的詳細(xì)介紹，以及警局裝修項(xiàng)目余下部分的公開展示。

Starting off with the rework of Station, being the first map that many if not all players will see meaning it’s an incredibly important first impression, perhaps even more so since the atmosphere of the Station and the decrepit state of the players surroundings reflects the state of Los Suenos as a whole.

讓我們首先從警局的返工重制說起，作為多數(shù)玩家，甚至可以說是所有玩家可能會(huì)看到的首張地圖，它給人的第一印象至關(guān)重要，更有甚者，警局的氛圍同玩家周圍破敗不堪的環(huán)境是對(duì)Los Suenos市整體狀態(tài)的一處縮影。

With the combination of clearly repurposed rooms, crumbling architecture, all filled with high tech police equipment in stark contrast of their surroundings it effectively communicates the state of the world the player has chosen to immerse themselves in.

通過欲要分崩離析的建筑、改造后的房間和高科技警用設(shè)備相結(jié)合，與玩家周圍環(huán)境形成鮮明對(duì)比，這些不僅向玩家傳達(dá)了所在地的世界觀，也讓游戲世界更加真實(shí)、豐富，同時(shí)更容易讓玩家投入其中，享受進(jìn)一步的沉浸體驗(yàn)。

The Station Reborn | 浴火重生的警局

Along with many the facelifts that many of the legacy maps are getting, we had no intention to leave our players' home base in the dirt. Teasing the level of detail we plan to put into the rest of the station with part 1 of the revamp we launched with Adam, we figured it was time to tease the rest of the work we’ve put into the map.

隨著許多玩家們爛熟于心的老圖改頭換面，我們也沒有忘記繼續(xù)改進(jìn)玩家的基地。在Adam更新一齊開展的改造計(jì)劃第1部分中，我們?cè)故具^警局計(jì)劃改造的其他部分?，F(xiàn)在是時(shí)候驗(yàn)收余下那部分工作的成果了。

In the future, many locations within the station will have more nooks and crannies to explore and perhaps even easter eggs to find. Many will be like the well known hats and the coffee machine, some will have more effect on you, your officers, and maybe even your gameplay.

很快，警局內(nèi)的許多地點(diǎn)將有更多隱蔽的角落同秘密空間可供探索，自然會(huì)有一些彩蛋等著被發(fā)掘。其中有些將像那些廣為人知的訓(xùn)練場大檐帽或是加速buff咖啡機(jī)?圖一樂，而有些則可能對(duì)你、你的團(tuán)隊(duì)，甚至整個(gè)游戲的游玩體驗(yàn)產(chǎn)生更多影響。

A Lesson on RONs AI with Ali | 與Ali齊上一節(jié)嚴(yán)陣以待AI課

By popular request, one of the most influential voices in the programming of Ready or Nots’ AI, Ali, has given us a writeup on exactly how the games AI functions and makes decisions based on environmental factors.

響應(yīng)廣大網(wǎng)友要求，嚴(yán)陣以待AI編程組最最具影響力的一員，Ali，為我們撰寫了一份關(guān)于本游AI如何根據(jù)環(huán)境因素進(jìn)行決策的課件。

The AI system we use for them is what is called a Utility-Based AI System. A decision making model.The concept behind Utility AI is to mathematically model human behavior in a computer program using numbers, formulas and response curves. The “human” we want to model is called an agent or simply, an AI. Each agent in a world environment has a list of Decisions (or what we call, Actions) they want to make. Every frame we run through the list of actions and decide which one to pick and execute. That’s the core AI loop.

“我們?yōu)閲?yán)陣以待的所使的是一套基于Utility(效用)函數(shù)的AI系統(tǒng)，即一個(gè)決策模型。效用函數(shù)AI其背后的原理就是計(jì)算機(jī)程序使用數(shù)字、公式和反應(yīng)曲線對(duì)人類行為進(jìn)行數(shù)學(xué)建模。我們想要模擬的 "人 "被稱為智能主體(agent)，或者簡單地說，是AI。世界環(huán)境中的每個(gè)智能主體都有一個(gè)他們想要做出的決定（或我們稱之為行動(dòng)）的清單。每一幀，我們都通過行動(dòng)清單，決定選擇和執(zhí)行哪一個(gè)。這就是核心的AI循環(huán)(core AI loop)。

Inside each action, there is a list of Considerations that make up what an action is mathematically, and is what ultimately determines the score for that action. Each consideration outputs a score, which all then get accumulated (using a scoring method) at the end to output a final score for that action, that will then be later used to pick the highest scoring action. That action will be the one to execute and run (using another system called the Activity System), with a commitment time set, so they don’t oscillate in their decision making, acting indecisive from frame-to-frame. There is also a thing called Commit Interrupts, it is a list of names of actions that can interrupt the current action if it is being committed to, and they get evaluated every frame too. Gives us more control.

在每個(gè)行動(dòng)中，都有一個(gè)考慮因素的清單，這些考慮因素在數(shù)理上構(gòu)成了該行動(dòng)，并最終決定了該行動(dòng)的得分。每個(gè)考慮因素都會(huì)輸出一個(gè)分?jǐn)?shù)，這些分?jǐn)?shù)最終會(huì)被累計(jì)（使用一種評(píng)分方法(scoring method)），以輸出該行動(dòng)的最終得分。然后，會(huì)從中選擇得分最高的行動(dòng)，并且該行動(dòng)將被執(zhí)行和運(yùn)行（使用另一個(gè)稱為“活動(dòng)系統(tǒng)(Activity System)”的系統(tǒng)）。為了避免決策上的搖擺不定，每個(gè)行動(dòng)都設(shè)定了一個(gè)決策時(shí)間(commitment time)，以免在每一幀之間表現(xiàn)得優(yōu)柔寡斷。同時(shí)，還有一項(xiàng)稱為“決策中斷(Commit Interrupts)”的功能，它是一串行動(dòng)名稱的列表， 并可以用來中斷當(dāng)前正在決策的行動(dòng)，游戲中每一幀都會(huì)被評(píng)估，以便提供更多的控制。

The scoring method is just a list of 4 math operations, Additive, Subtractive, Multiplicative and Divisive. Really simple, they just add, subtract, multiply or divide into a score accumulator. It serves as another setting that designers can tweak to mold the final action score.

評(píng)分方法(scoring method)只是一個(gè)由 4 種基本數(shù)學(xué)運(yùn)算組成的算法，包括加法、減法、乘法和除法。它們只是在分?jǐn)?shù)累積器(score accumulator)中進(jìn)行加減乘除,很簡單的捏。它的另一個(gè)作用就是，游戲設(shè)計(jì)師可以隨時(shí)通過調(diào)整該累計(jì)器來調(diào)整最終的行動(dòng)得分。（P.s.換句話說，他想讓嫌疑人激進(jìn)的點(diǎn)可以直接干拉猶豫值到最低和行動(dòng)值最高，之前坊間有修改器能直接操作來著）

One example of what a consideration can be is “Health”. This is scored using a?Scoring Function that we define, that scoring function takes in a world context and outputs a number. In this case, “Health”, it is really simple, we do Current/Max, which gives you a value (or in other words a score) from a sliding scale between 0.0 to 1.0, which then can be optionally scaled by a?Weight to bias the action in the AI if we want. You can imagine other considerations having more complicated functions. The output of the scoring function is then mapped by the response curve and is used as the final score of the consideration. There are many curve presets to choose from, the default is a Linear response, but you can have all sorts of easing curves, like Exponential, Sinusoidal, etc. (or even a hand made curve, or even better, we have the ability to implement your own custom curve function, which is pretty neat.)

考慮因素的一個(gè)例子是“健康值”。我們使用一個(gè)評(píng)分功能(Scoring Function)，該功能從對(duì)當(dāng)前游戲內(nèi)一列的操作評(píng)估后，輸出一個(gè)數(shù)字，以對(duì)“健康值”進(jìn)行評(píng)分。在這種情況下，“健康值”評(píng)分的計(jì)算非常簡單，我們將當(dāng)前值除以最大值，得到一個(gè)介于0.0和1.0之間的值（或者說是分?jǐn)?shù)），這個(gè)值可以通過一個(gè)權(quán)重(Weight)進(jìn)行縮放，以便AI偏向于行動(dòng)列表種的某個(gè)行動(dòng)，當(dāng)然，前提是如果我們需要的話。其他的考慮因素可能使用更復(fù)雜的函數(shù)進(jìn)行評(píng)分。評(píng)分函數(shù)的輸出值隨后被映射到響應(yīng)曲線上，并作為考慮因素的最終得分。有許多不同的響應(yīng)曲線可供選擇，其中默認(rèn)的是線性響應(yīng)曲線，但你也可以使用各種緩動(dòng)曲線，如指數(shù)曲線、正弦曲線等等。（甚至可以使用任何自定義的曲線函數(shù)，你看看這多贊啊是不是。）

A health of 0.9, (altered by the shape of a curve) can be a good or bad thing. Good being you probably don’t need to take cover and you can risk taking certain actions. Bad thing if you want your health to always stay full, so you find cover and regenerate using a stim (this doesn’t happen in RON of course, but just as an example of how looking at the same number can mean two different things).

當(dāng)健康值為0.9時(shí)，(根據(jù)函數(shù)曲線形狀進(jìn)行調(diào)整)?可能是好事或壞事。好事是你可能不需要尋找掩體并且繼續(xù)冒險(xiǎn)嘗試某些行動(dòng)。壞事是如果你想讓自己的健康一直保持滿值，那么你需要找到一個(gè)相對(duì)完整切安全的掩護(hù)并使用類似腎上腺素針進(jìn)行恢復(fù)（當(dāng)然，這在嚴(yán)陣以待中是不會(huì)發(fā)生的，介只是作為一個(gè)如何看待相同數(shù)字可以意味著兩種不同的東西的例子）。

As you can imagine with a library of hundreds of considerations, using those and tweaking response curves is how we can get different “personality types” or behavior's out of our suspects and civilians, some being more resistant to surrender than others or some being more eager to take cover or some that can be more suicidal if you don’t de-escalate the situation quickly.

正如你所想象的，通過使用數(shù)百個(gè)考慮因素并調(diào)整響應(yīng)曲線，我們可以從我們的嫌疑人和平民中獲得不同類型的?"人格" 或其行為模式，其中一些人比其他人更加拒捕，或者一些更偏好于尋找掩體，還有例如如果你不能迅速控制場面，就會(huì)選擇自殺的那類等等。

We have another concept in our system that I don’t think I’ve seen anywhere in other people’s utility implementations but it’s hardly anything groundbreaking. We call it Gates. It acts as a supplementary aid to Considerations. Gates are similar to considerations but instead of returning a score, they just return true or false, open or closed. The purpose of gates is to block off actions that we don’t want the AI to consider. All gates must be open for the action to be considered for scoring. Cooldown timers are one example where gates are useful, there is no way you can reasonably do this with considerations and weights, it needs to be a binary operation. Has the cooldown finished or not? The cooldown must be finished before considering said action. Before when we didn’t have this concept, we would sometimes have AI surrender to no stimulus or do certain actions that they shouldn’t have been doing, and when you went to debug what was going on, all the scores seemed reasonable and you could see why they did a certain thing. Gates help us control them a little more so as to not go wild and do un-expected things, which our designers like.

我們的AI系統(tǒng)中還有另一個(gè)概念，我好像冇在其他人的程序?qū)崿F(xiàn)睇過，但這也并不是什么很具有突破性的東西。我們稱之為門控(Gates)。它作為對(duì)決策考慮的補(bǔ)充輔助。Gates類似于考慮因素(Considerations)，但它們不是返回分?jǐn)?shù)，而是返回true或false，開啟或關(guān)閉。門控的目的是阻止我們不想讓AI考慮的行動(dòng)。為了評(píng)分，所有門必須保持開啟狀態(tài)。使用門控的一個(gè)實(shí)用例子是冷卻時(shí)間，因?yàn)檫@無法通過考慮因素和權(quán)重進(jìn)行合理處理，必須進(jìn)行二進(jìn)制操作。判斷是否結(jié)束冷卻時(shí)間，必須在考慮該行動(dòng)之前完成冷卻。在我們沒有這個(gè)概念之前，AI有時(shí)會(huì)沒有刺激地投降，或者做一些不應(yīng)該做的事情。當(dāng)你試圖調(diào)試時(shí)，所有的得分看起來都很合理，你可以理解它們?yōu)槭裁磿?huì)做某些事情。門控可以幫助我們更好地控制AI，避免它們做出意料之外的行動(dòng)，這是我們的設(shè)計(jì)師們喜歡的。

The simple concept of using math and formulas to mold the output result to your liking (using weights, different scoring methods and response curves), gives you a whole bunch of states that you can’t possibly program into the AI manually, which in turn greatly affects the gameplay experience, each AI will behave slightly different than the next. Our previous version of the AI system was basically one large state machine at its core and can only ever be worked on by us, the programmers. If we wanted to progress, implement better AI, allow for designer freedom to alter AI behavior quickly in the editor, without asking programming to do simple things, a radical approach was needed to switch to Utility (which took about 6 months to implement).

利用數(shù)學(xué)和公式來塑造輸出結(jié)果的簡單概念（使用權(quán)重、不同的評(píng)分方法和響應(yīng)曲線），使得你可以得到許多無法手動(dòng)編程到AI中的狀態(tài)，這反過來極大地影響了游戲體驗(yàn)，每個(gè)AI的行為都會(huì)略有不同。我們之前版本的AI系統(tǒng)基本上是一個(gè)大號(hào)狀態(tài)機(jī)(state machine)，只能由我們程序員親自來開發(fā)。如果我們想要進(jìn)步，實(shí)現(xiàn)更好的AI，一個(gè)允許設(shè)計(jì)師在編輯器中快速修改AI行為而不需要編程基礎(chǔ)的AI，就需要采用一種激進(jìn)的方法來轉(zhuǎn)向Utility算法系統(tǒng)（這需要大約6個(gè)月的時(shí)間來實(shí)現(xiàn)）[拖更Flag]。

However, we still use the state-machine concept for concrete actions like, moving to and picking up a weapon on the floor or breaching a door (which has a lot of states). But the decision making aspect of the AI, the brains if you will, is all Utility-Based, we don’t have to manually say “if this then that, if this then do x”, instead, as outlined above, we give them a set of actions and rules to follow, then they can go about into the world and make decisions for themselves which is pretty neat and what feels like “true” AI.

然而，對(duì)于像移動(dòng)到某處并在地板上拾取武器或破門而入（狀態(tài)較多的行動(dòng)）等具體動(dòng)作，我們?nèi)匀皇褂脿顟B(tài)機(jī)這一概念操作。但是AI的決策方面，也就是所謂的大腦部分，全部采用了基于Utility的方法，我們不需要手動(dòng)編寫 “if this then that, if this then do x” 的規(guī)則。相反，我們使用上述提到的方法，為它們提供一組要遵循的動(dòng)作和規(guī)則，然后它們可以在游戲世界中自主做出決策，不覺得這很酷嗎? 作為一名理工男我覺得這太酷了，很符合我對(duì)AI的想象，科技并帶著趣味。

All we have to do on our side in the editor is setup their Archetype (the definition/personality of an AI), which houses the actions we want them to make. After we’re done, we assign the archetype to the specific AI in our data tables. We have about 50 archetypes and many AI variants in a level share a similar or the same archetypes, for many reasons, for simplicity sake, we don’t want to duplicate archetypes unnecessarily when we can just reuse the ones that work, or if we happen to take a liking to a particular archetype in the way they behave, etc., plus the overhead of our team in managing all those archetypes, it’s something we try to stay lean with.

在編輯器中，我們所要做的就是設(shè)置他們的原型（AI的基本定義/個(gè)性），這其中包含我們希望他們做出的行動(dòng)。完成后，我們將原型分配給我們數(shù)據(jù)表中的特定AI。我們有大約50個(gè)原型，一個(gè)關(guān)卡中的許多AI變體都有類似或相同的原型，原因有很多，為了簡單起見，我們避免原型不必要重復(fù)，而是盡可能重復(fù)使用那些有效的原型。如果我們對(duì)某個(gè)特定原型的行為模式感到滿意，我們也會(huì)喜歡重復(fù)使用它。加之我們團(tuán)隊(duì)所有這些原型的開銷，這是我們?cè)噲D保持精益精干的一套AI邏輯原因。

Conclusion | 總結(jié)

This concludes our 50th briefing. Be sure to tune in next time for more development news!

這便是第五十期雙周簡報(bào)的全部內(nèi)容了。請(qǐng)?jiān)谖磥砝^續(xù)關(guān)注我們的開發(fā)進(jìn)度報(bào)告！

If you’d like to help us test new content during playtesting periods on the Supporter exclusive experimental branch, provide us with your game feedback, and keep up with the Supporter community; you can become a supporter at?www.voidinteractive.net?or at our Steam store page.

如果您想在每次都能玩到支持者版本專屬更新，抑或是向我們提供您的游玩反饋，并加入支持者專屬社區(qū)，請(qǐng)?jiān)?www.voidinteractive.net?或嚴(yán)陣以待的 Steam 商店頁面上購買支持者版本DLC

Are you a content creator on youtube or twitch looking for new games? We got you covered; Ready or Not has partnered with Lurkit to elevate gameplay! Make sure to follow us here.

你是一位正在尋找新的游戲探索的 YouTube 或 Twitch 上的內(nèi)容創(chuàng)作者嗎？來吧，加入嚴(yán)陣以待，我們已經(jīng)與 Lurkit 創(chuàng)作者平臺(tái)達(dá)成合作關(guān)系！請(qǐng)務(wù)必在此關(guān)注我們

Make sure you follow Ready or Not on Steam here.

確保你已經(jīng)在steam上關(guān)注了嚴(yán)陣以待哦:)

Keep your feet on the ground.

保持聯(lián)系

VOID Interactive

你的倉鼠小鴿子VOID醬<3

The End. 我好困...zZZ?01/04/2023? Trans by OC in UK