Auto balancing Decision trees aka Resource Trees II

Just continuing from last time.

So after we called Act on the economy node it checks that it isn't a leaf node and therefore it checks it resources.

Auto balancing Decision trees aka Resource Trees I

Today were gonna talk about a process for decision making that you can use for higher level Ai functions in games. Basically this method can handle purchases, tech level up. In some case even goal selection for a computer opponent. I will discuss this in the context of trying to make an Ai for an RTS game but the same principles will hold for a turn based strategy game and depending on context in a lot of other games too.
So the basic idea here is the following. Computers are really good at managing tree structures we have tons of efficient algorithms that works well with them and we can search in them easily. This however is not just limited to existing algorithms, basically any kinda of data is normally easy to analyze if you can represent it as a tree.

This comes from the fact that when you represent data as a tree you have already performed a divide and conquer on that data to divide it into smaller parts. Those making the code to crunch the data for each node of the tree a lot simpler than if you had to crunch all that data in one piece.

Still alive here

3D:

Shadow Maps
This is pretty much the standards for doing shadows in games nowdays, and the dafacto standard for doing them for movies, Shadow buffers in difference form shadow volumes is image based and can therefore handle alpha masked polygons like a fence, It also allows techniques for blurring or otherwise distorting the shadows.

Percentage Closer Filtering 
A problem with shadow maps is that they easily get pixelized even with the best intentions. Percentage closer filtering is a technique to combat those jaggies and making your shadow edges soft, We will look at different implementations for example randomised kernel rotation with a poisson disc.

Percentage Closer Soft Shadows
In the real world shadows become softer and softer the further they come from the object casting the shadow. This is how to do the same in games using a technique that builds on the Percentage Closer Filtering technique.

Slight hiatus

Due to me getting married and going on my honey moon game school gems will have a short 3 week hiatus until I return.

Wish me luck :)

Code Standards - Why we need them

It might be a bit far fetched to call this a gem, at the same time for those who have never worked in a team before or are trying to organize one this might be life saving. I also feel that I need to add some more articles that cater more to the beginning programmer, while these sorts of articles won't become the standard for the site they will appear from time to time.

So what is important about code standards you may ask ? There are two main cases, one is when working with other people and the other is when you are working with code you haven't touched in a along time. The problem with a lot of code you find on the net and tutorials are that they are inconsistent in how the code looks or hard to read, obviously that lats part is subjective when you are used to it any way of coding might be easy to read. But when you are working with code you haven't touched in a long time you are unlikely to remember all the details of that code so any code standard that makes the code easier to read and understand helps.

Influence Maps II - Practical Applications

Now it is time to look at how we can use influence mapping in a practical application showing how we can use a simple influence mapping based scheme to create an AI that reacts surprisingly realistic and intelligent as long as it works within certain perimeters and you aren't trying to control it too precisely. This test as I have implemented it will only be using the basic Influence map but we will discuss interesting possibilities of using the other maps too. I am also sure that you by yourself will imagine a ton of ways to use the maps and even create new maps after these two articles. But for now we look at creative ways to use what we have learned so far.

What we are going to be looking on is what we used early on as an experimental Ai for Ground Control 2 it actually become the basis for that AI though heavily modified, partially because it did defeat our project lead 1 on 1 but mostly because the single player team needed a lot more control to create interesting single player missions. But within it's limitations it performed admirably and as I mentioned did become the basis of the AI.

Influence Maps I

In artificial intelligence one of the biggest problems is how to convert a complex world into a set of data that the AI can actually understand and work on. The real world or even a game world is infinitely complex and different techniques to compress that infinite data set into a usable format is a classic problem in ai research. Thankfully for games we don't have the trouble of having to interpret the image from a camera to create a 3dimensional world to move around in.

We have the advantage of having access to extremely detailed data of everything in the game. But the amount of data we have access to makes any sort of planning by the means of calculations impossible. The world is simply to complex, so in order to be able to work with it we have to employ a series of different algorithms the one we are going to look at today is called influence mapping and it's a way to resolve the actual strength relation ship on a map and helps to base tactical decisions on this. It has nothing to do with purchase planning or high level strategical decisions It's simply a analysis tool to create an accurate and usable view of the world.

Influence maps are based around the concept that objects in the game influences the strength relationships between the players and this influence spreads from their current position outwards throughout the map. If you add in all the influence of all the objects then you would get a view of the relative strength relationships for that part of the map. You should also be able to detect the actual front lines between the different sides with it too. As we will show later on we are able to calculate a lot of different data from the influence maps but for now I think it is time to go back to explaining how this all works. But as a tease in the end of this we will discuss how to create a simple army level opponent behaviour by only using influence maps. There will be limits to it of course but it is still pretty cool.