Sound the alarm! A post on AI and spatial access methods

Preamble

Firstly to avoid confusion, I know I often refer to entities in the game as Spatials (as that is the object I use) but with this post the spatial in the title is referring to an objects location in 3D space.

So recently I've paused work on asset creation and have taken to working on something far more interesting, my game AI. I've programmed up some basic decision making to allow the AI to work out if it's scared, confident or concerned and to what level but that's not what this post is about. This post is about what happens when an enemy NPC discovers the player and raises an alarm.

So raising an alarm could be done in numerous ways and could have varying consequences:

• A physical alarm is rung and either through the alarms location, loudspeaker, or general flashing lights the players rough location is broadcast to all enemies in a large area.
• The enemy that spotted the player shouts over radio and alerts all other enemies the players precise location, unless they lose sight of the player in which case where the player was last seen.
• The enemy shouts out hoping that someone hears and comes to assist.

I've been toying with all three ideas and I'm thinking of doing a combination of 1 and 3, with the NPC having the choice of shouting and engaging in combat or running scared to an alarm and hoping for backup. Then environmental properties such as distance to the nearest alarm would factor in the decision making and make the combat less predictable, something which is always good.

So now that's decided and it comes to implementation an important question presents itself.

How do I find all the enemies within a given radius?

This is important for the third method as the NPCs need to hear the shout, so what were my initial thoughts:

• Using a spatial access method such as a binary space partition or a k-d tree.
• Creating a bounding box centred on the location of the sound and extending out to its range and checking for the NPCs that fall within the box

Now the first method can require a fair bit of programming and also every time an NPC moves the bsp or k-d tree would need to be regenerated. However it allows to quickly determine which NPCs are within range.

The second method on the other hand is very simplistic to program and maintain but comes with a performance drop. It would iterate through every NPC checking for intersection whereas the previous method would only look at a subset.

Now at this point hash maps had popped into my mind but I'd cringed and thought about iterating through them checking for every continuous point in 3D space within the range. Which is just icky.

So still undecided I did some googling and suddenly team hash map presented a winning pitch. They keys I'd use would be discrete points and I'd split the map into cubes of a set size [source: http://goo.gl/dtXSfw ]

Now for my hash map the key will be an array of integers and the object a list of spatials (the entity sort). And as non-primitives in java are passed by reference not value to update the hash map I decided to:

• Iterate through the values in the hash map (so loop through all the array lists)
• Get each spatials key based on current position
• Check if the array list stored at that key is equal to the one I'm at in the iterator.
• If not add the spatial to the array list at that key and remove it from the array list it was in.

Now worst case that should perform at O(n) where n is the number of entities. And getting all the entities within the range of a sound should be O(log(n)) as we are only dealing with a subset of the entities rather than brute forcing through every entity.

All in all I think this was the best way to do it, but we'll see when I start testing it out and nearer the end when I start stress testing my game to see what it can handle in terms of how many NPCs active at once etc.

A word on sound intensity

So I've probably (hopefully) mentioned it before (at least in passing) but I'm aiming on using a form of utility in my AI where scores are assigned to attributes and actions and used to determine the best result. So from this some new inputs to the AIs decision making process is born, sound intensity and context.

So initially my thoughts on sound intensity would be that it would most likely be an exponential decay, like most things that drop off in nature. But in the name of science I decided to have a proper look into it. 

This lead me to this formula [Source: http://goo.gl/FQ9sEL ]:

Where L2 is the sound intensity we want to work out and r2 the distance it is from the sound

L1 is a reference intensity and r1 how far that is from the sound.

I suppose the reference is there to try and take account for the varying acoustic properties of rooms but it's not really that relevant (after all I posted an excellent link for those that want to learn more!) What is relevant is that from the graphs on the page and the sum we can see that I wasn't far off, I can model this with an exponential decay.

This decay will represent the intensity of the sound where the AI is based on the distance from the sounds origin and how intense it is at the origin:

The perceived intensity will be normalised between 1 and 0 where 1 is definitely hearing something of note and 0 is hearing nothing of note. Whether or not the NPC responds to the sound at values between 0 and 1 depends on its own attributes such as alertness etc.

I should also take into account any instances of background noise that could dilute the intensity of the sound but that's a stretch goal for now.

Once I've done with this fleshing out of AI I should go back to graphics and design and maybe even be able to provide my first game play video... Not sure if that's a goal for the distant or not too distant future though.