Name: Designing a Better Aim Assist for 2D Games
Uploaded: 2022-10-08T08:07:18.000Z
Duration: 10 min 45 s
Description: VoiceTubeの動画で発音を聞きながら英語表現を覚えよう！学べる英語：

様態の副詞

Something that's been on my mind recently is the  implementation of aim assist in fixed-perspective  

games like Hades and Death's Door,  when aiming with an analog stick. 

When playing these games, I've had  experiences where I felt a disconnect  

between what I wanted to do, and what the  game's aim assist thought I wanted to do. 

Fundamentally, I felt like my issue was  that many of these games implement more  

of an aim automation system than aim assist. The effect is much more pronounced in Hades,  

where the weapon can snap by as much as 90  degrees, wrestling control away from the player. 

In Death's Door, the effect is much more  subtle, as the game never visualizes the  

correction it applies to the user input,  and doesn't correct by more than 15 degrees. 

For the sake of this video, I reverse engineered  the aim assist code in both of these games,  

because I think it highlights the  types of gameplay design problems  

that aim assist attempts to address. I'll then present my own thoughts on  

how to better formulate the problem of  aim-assist in a way that makes it easier  

to think about how to balance things like  player intention, skill, and gameplay feel. 

Hades is a fast-paced game where players often  find themselves surrounded by fast moving enemies 

In general, every enemy must be dealt with  before the player can progress to the next room 

In this context, aiming is often secondary  to hitting, and so the aim assist in Hades  

appears to have been designed to ask the question  “What are the targets that the player might want  

to hit in this context”, and, “Which one is  the closest to the player's aim direction?” 

In theory, this ensures that any target  the game selects will always make  

incremental progress to clearing the room. Weapons which require line of sight will  

only snap to targets that have line of sight. Weapons won't snap to enemies that are off screen,  

that are currently invulnerable, or that are  outside of the range of the player's attack. 

For each potential target, the game uses the  current gameplay context to make its best estimate  

of whether that target is valid for aiming or not. For the targets which pass, the game selects  

the target whose direction is closest to the  player's input direction, and whose distance  

to the player and arc distance to the player's  input direction falls below some threshold. 

In Death's Door, the aim assist is  formulated in the exact opposite order. 

It first asks, “What are the hittable objects  closest to the player's aim direction”,  

then, “Which one is the player  most likely to want to hit?” 

This reversal is interesting because it allows  for multiple layers of context sensitivity. 

For example, puzzle elements have  higher priority than nearby enemies,  

which in turn have higher  priority than faraway enemies. 

Because both of these games snap the  player input directly onto a target,  

any mis-snap can lead to an instantly  frustrating experience for the player. 

This is mainly an issue when the player has a very  specific target in mind, because the incongruity  

between player intention and avatar action  leads to a very noticeably jarring experience. 

A common failure mode is when the player  intends to shoot at a target which is  

slightly out of range, but the aim assist  erroneously corrects the player aim to a  

completely different in-range target. This incongruity between intention and  

action can be especially frustrating if aiming at  the wrong target doesn't make any tactical sense,  

such as prioritizing a low-threat  enemy instead of a high-threat enemy,  

using a high-power attack on a weak enemy,  or using an AOE attack on a lone enemy. 

Aim assist is something that I  want to incorporate in my own game,  

and through some early prototyping,  testing, and subsequent reflection,  

I've come up with a few ideas that I've  consolidated into what I think is a pretty  

flexible framework for thinking about the problem  and potentially addressing some of these issues. 

Let's represent player input  direction and avatar orientation  

as the x and y axes on a 2D phase graph. The map between player input and avatar  

player input is mapped to avatar  orientation directly without modification. 

The avatar points in exactly the direction of  input, as is the case when there's no aim assist. 

When f(x) is not equal to x,  it describes the transformation  

from the player input space to  the avatar orientation space. 

Now let's consider a potential  target near the player. 

The target has a particular angular position  and size from the player avatar's perspective 

The angular position changes as the  target moves around the player avatar. 

The angular size changes with distance, and is  also correlated with the target's physical size. 

When f(x)=x, the range of avatar  orientations that are on target  

is equal to the range of controller  inputs that are on target. 

However, if we select f(x) to widen the target  region, a greater range of controller inputs  

get mapped to on-target avatar orientations.

In more mathematical language, we can call this the preimage

of the target region B under the function f, and we can think of it as being  

the virtual size of the target from the  perspective of the player's controller. 

Even for a physically small target, we can  choose to maintain a large virtual target,  

making it easier for the player to input an  angle that points to somewhere on the target. 

In this way, we can imagine constructing  f(x) to always maintain a minimum preimage  

size of each target, no matter  their actual size or distance. 

This allows us to set a skill floor for how  hard we want it to be to aim at targets. 

Another way of analyzing this  function is by looking at its slope. 

Regions of low slope represent  regions where the function makes  

it easier to aim by decreasing sensitivity.

They must be balanced by regions of high slope,

to ensure the total domain and range fill up the full 360 degrees.

Beyond this constraint, we can make many  different decisions for the design of f(x)  

depending on our goal as a gameplay designer. For example, we may like to smooth the curve to  

reduce the jarring effect of a sudden change  of sensitivity as we aim toward a target. 

Perhaps for a precision weapon, we  want the player to feel a change in  

sensitivity at the center of the  target no matter how large it is,  

so we design f to always flatten  the slope at the center of targets. 

Perhaps we want to limit the extent  to which we enlarge the virtual size  

of targets on higher difficulty settings. Or to only provide the feeling of locking  

on target without actually affecting the  difficulty of hitting targets at all. 

Maybe we decide that for predictability, we want  to apply the same correction to all targets no  

matter how big or small they are. And in each case, we can globally  

decide how much assistance to provide by  blending it with the identity function. 

As a point of comparison, the sort of direct  snapping aim assist approach we've previously seen  

looks something like this in our visualization. We see that this indeed produces large virtual  

targets, which any useful aim  assist function should do. 

However, the direct snapping approach also  produces large output discontinuities,  

and sets of avatar orientations whose  preimage is completely empty, representing  

directions that are impossible to aim at. Consequently, any aim assist system using  

the snapping method must be highly  robust in its target identification,  

as any failure of the system results  in catastrophic gameplay consequences. 

The generalization of aim assist to include  these smoother functions thus represents a  

relaxing of the responsibility  of the aim assist algorithm,  

while still addressing the fundamental problem  of compensating for imperfect player inputs. 

In our search for the perfect function  that satisfies our game design goals,  