Potential Fields is an obstacle avoidance and general path-finding technique that has only quite recently started to be used in AI for video games. It has previously mainly been used in robotics for robot navigation. Although quite unexplored, Potential Fields have so far worked well in video games. Previous research has mainly focused on RTS (Real-Time Strategy) games. This research explores the use of Potential Fields in another genre called arena games (which is a quite unexplored genre as well). In the implementation, multiple Potential Fields have been used together, where each field had a different task. Also, weights were used on the different Potential Fields to give them different importance depending on some factors that are dynamic through the game, hence the use of the word weighted. The main focus of the user studies conducted was the impact the weights had on the computer controlled unit's general behaviour. The user studies conducted showed that it was hard to determine who was a computer controlled character and who was human controlled, therefore telling that multiple Potential Fields worked well for movement. The test participants became better at determining this the second match they played, no matter the properties of the match. However, the user studies did not show that the weights made a remarkable difference; there was no significant improvement on the situation adaptation and team cooperation, but no deterioration either. The concept of using weights needs to be explored further.