previous signature is used for angles. This vector is

![0_image_0.png](0_image_0.png) then normalised.

where t is the current time step and t + 1 is the following one, time steps occurring every 125 ms. −→V **is the**

![0_image_1.png](0_image_1.png)

velocity vector without its z component, −→D **is a vector**
pointing toward the character's aiming point without its z component (figure 1). The z **component was ignored**
for the signatures because it simplifies the signatures without losing too much information.

## The Experiment Is Composed Of Two Steps:

1. 6 low/medium level human played a **deathmatch**
game during 20 minutes during which signatures were computed. We chose a duration of 20 minutes because signatures tend to stabilise after 15 to 20 minutes. A deathmatch **follows the basic rules**
and the objective is to "kill" as many other players as possible. Each human plays using a keyboard and a mouse to control the virtual character and a classic desktop computer screen to see the virtual environment.

2. Signatures are computed for 8 agents having 8 different skill levels. Those agents fight against each other in 6-agent matches in the same topography, following the same rules and during the exact same time.
We used tools developed by our team and based on Pogamut 2 (Burkert et al. 2007). They are available at svn://artemis.ms.mff.cuni.cz/pogamut/branches/fabien_tence.

The experiment gave us 2 signatures for each of the 14 subjects, each signature having 20 components. A sample of those results is given in figure 2.

## Results' Analysis

In order to visualise the data, we chose to do a Principal Components Analysis (PCA) on the signatures.

Subjects are represented using the two principal components in figures 3 and 4.

![0_image_2.png](0_image_2.png)

The first chart (figure 3) represents very well the data with about 90% of the variance represented. The first PCA axis is negatively correlated with brutal angle changes and the second PCA axis is positively correlated with small angle changes. The second chart (figure 4) is less accurate with only 78% of the variance represented; however, it does suffice to extract some interesting data's features. The first PCA axis is globally negatively correlated with walking backwards and backwards+sideways. The second PCA axis is positively correlated with walking forward+slightly sideways and negatively with forward+sideways.